Image directed search

ABSTRACT

An image recognition approach employs both computer generated and manual image reviews to generate image tags characterizing an image. The computer generated and manual image reviews can be performed sequentially or in parallel. The generated image tags may be provided to a requester in real-time, be used to select an advertisement, and/or be used as the basis of an internet search. In some embodiments generated image tags are used as a basis for an upgraded image review. A confidence of a computer generated image review may be used to determine whether or not to perform a manual image review. Generated image tags are optionally used for sorting specific domains such as the inventory of a vendor.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority and benefit of U.S. provisional patentapplication Ser. No. 62/180,619 filed Jun. 17, 2015; this application isalso a continuation in part of U.S. non-provisional patent applicationSer. No. 14/592,709, 14/592,885 and 14/592,555 each of which were filedJan. 8, 2015, and are continuations-in-part of U.S. non-provisionalpatent application entitled “Image Processing,” filed May 1, 2014 andhaving Ser. No. 14/267,840 which, in turn, claimed priority to; U.S.provisional application 61/956,927 filed May 1, 2013, and each of whichfurther claimed priority to and benefit of the following U.S.Provisional Patent Applications:

-   -   “Visual Search,” filed Apr. 4, 2014 and having Ser. No.        61/975,691;    -   “Visual Search Advertising,” filed Apr. 7, 2014 and having Ser.        No. 61/976,494;    -   “Image Processing,” filed May 1, 2014 and having Ser. No.        61/987,156;    -   “Real-time Target Selection in Image Processing” filed Jul. 31,        2014 and having Ser. No. 62/031,397;    -   “Distributed Image Processing” filed Oct. 27, 2014 having Ser.        No. 62/069,160; and    -   “Selective Image Processing” filed Nov. 25, 2014 having Ser. No.        62/084,509.        All the above patent applications are hereby incorporated herein        by reference.

BACKGROUND

1. Field of the Invention

The invention is in the field of image processing, and more particularlyin the field of searching based on images.

2. Related Art

Image tags are identification tags (words or tokens) that characterizethe characteristics of an image. For example, For example an image of acar may be tagged with the words “car,” “Ford Granada,” or “White 1976Ford Granada with broken headlight.” These tags include varying amountsof information and, as such, may vary in usefulness.

SUMMARY

Embodiments of the invention include a two pronged approach to taggingof images. The first prong is to perform automated image recognition onan image. The automated image recognition results in a review of theimage. The image review includes one or more tags identifying contentsof the image and optionally also a measure of confidence representativeof the reliability of the automated image recognition. The second prongin the approach to tagging of images includes a manual tagging of theimage. Manual tagging includes a person viewing each image, consideringthe content of the image, and manually providing tags representative ofthe content of the image. Automated image recognition has an advantagein that the cost, in time or money, of analyzing each image can berelatively low. Manual tagging of images has an advantage of higheraccuracy and reliability.

Embodiments of the invention combine both automated image recognitionand manual image recognition. In some embodiments automated imagerecognition is performed first. The resulting image review typicallyincludes both one or more tags characterizing the image and a measure ofconfidence in the accuracy of these tags. If the confidence is above apredetermined threshold, then these tags are associated with the imageand provided as an output of the tagging process. If the confidence isbelow the predetermined threshold, then a manual review of the image isperformed. The manual review results in additional and/or different tagsthat characterize the contents of the image. In some embodiments, theautomated image recognition and the manual review of the image areperformed in parallel. The manual review is optionally cancelled oraborted if the automated image recognition results in one or more tagshaving a confidence above the predetermined threshold.

In some embodiments recognition of an image can be upgraded. Upgradingof the image recognition process includes a request for further orimproved tags representative of the content of the image. For example,if automated image recognition results in the tags “white car,” anupgrade of this recognition may result in the tags “white Ford Granada.”In some embodiments, an upgraded review makes use of an expert humanreviewer. For example, the above example may include the use of a humanreviewer with an expert knowledge of automobiles. Other examples ofreviewer expertise are discussed elsewhere herein.

Various embodiments of the invention include features directed towardimproving the accuracy of image recognition while also minimizing cost.By way of example, these features include efficient use of humanreviewers, real-time delivery of image tags, and/or seamless upgrades ofimage recognition. The approaches to image recognition disclosed hereinare optionally used to generate image tags suitable for performinginternet searches and/or selecting advertisements. For example, in someembodiments, image tags are automatically used to perform a Googlesearch and/or sell advertising based on Google's AdWords.

In some embodiments image tags are processed so as to increase theirutility for searching in restricted domains. The processing can include,for example, modification, prioritization and/or weighting of tokens(e.g., words) within image tags. In some embodiments, image tags aremodified so as to increase the utility of searches made using the tags.For example, for an image tag including multiple tokens, one token maybe given greater weight relative to another token. Search resultsgenerated using these tokens can then be weighted based on the weightingof the tokens.

In some embodiments, the weighting of tokens is based on a specificdomain. For example, a token “beaded” may have a greater weight in a“fashion” domain relative to a “plumbing” domain. Domains may be definedin terms of subject matter, as in the above example, or using othercriteria. Weighting of tokens is optionally determined by tagging ofknown images within a domain.

Various embodiments of the invention include a search system comprisingan I/O configured to communicate an image over a communication network;image tagging logic configured to provide image tags characterizing theimage, the tags including a plurality of tokens; a token rankerconfigured to parse the image tags so as to identify the plurality oftokens and to rank the identified tokens; a search engine configured toperform a search using the image tags; response logic configured toprovide results of the search, the results in an order responsive to therank of the identified tokens; and an electronic processor configured toexecute at least the token ranker.

Various embodiments of the invention include method of processing animage, the method comprising: determining a weighting of image tokens;receiving an image; identifying image tags characterizing the image;parsing the image tags to identify image tokens within the image tags;performing a search using the tokens; sorting results of the searchbased on the weighting of the image tokens; and presenting the sortedresults of the search.

Various embodiments of the invention include an image processing systemcomprising an I/O configured to communicate an image and image tags overa communication network; an automatic identification interfaceconfigured to communicate the image to an automatic identificationsystem and to receive a computer generated review of the image from theautomatic identification system, the computer generated review includingone or more image tags identifying contents of the image; destinationlogic configured to determine a first destination to send the image to,for a first manual review of the image by a first human reviewer; imageposting logic configured to post the image to the destination; reviewlogic configured to receive the a manual review of the image from thedestination and to receive the computer generated review, the manualreview including one or more image tags identifying contents of theimage; response logic configured to provide the image tags of thecomputer generated review and the image tags of the manual review to thecommunication network; memory configured to store the image; and amicroprocessor configured to execute at least the destination logic.

Various embodiments of the invention include a method of processing animage, the method comprising receiving an image from an image source;distributing the image to an automated image identification system;receiving a computer generated review from the automated imageidentification system, the computer generated review including one ormore image tags assigned to the image by the automated imageidentification system and a measure of confidence, the measure ofconfidence being a measure of confidence that the image tags assigned tothe image correctly characterize contents of the image; placing theimage in an image queue; determining a destination; posting the imagefor manual review to a first destination, the first destinationincluding a display device of a human image reviewer; and receiving amanual image review of the image from the destination, the image reviewincluding one or more image tags assigned to the image by the humanimage reviewer, the one or more image tags characterizing contents ofthe image.

Various embodiments of the invention include an image source comprisinga camera configure to capture an image; a display configured to presentthe image to a user; eye tracking logic configured to detect an actionof one or more eyes of the user; optional image marking logic configuredto place a mark on the image, the mark being configured to indicate aparticular subset of the image and being responsive to the detectedaction; display logic configured to display the mark on the image inreal time; an I/O configured to provide the image a computer network;and a processor configured to execute at least the display logic.

Various embodiments of the invention include an image source comprisinga camera configure to capture an image; a display configured to presentthe image to a user; eye tracking logic configured to detect an actionof one or more eyes of the user; image marking logic configured for auser to indicate a particular subset of the image and to highlight anobject within the subset, the indication being responsive to thedetected action; display logic configured to display the highlighted onthe image in real time; an I/O configured to provide the image and theindication of the particular subset to a computer network; and aprocessor configured to execute at least the display logic.

Various embodiments of the invention include an image source comprisinga camera configure to capture an image; a display configured to presentthe image to a user; selection logic configured for selecting; imagemarking logic configured for a user to indicate a particular subset ofthe image and to highlight an object within the subset, the indicationbeing responsive to the detected finger; an I/O configured to providethe image and the indication of the particular subset to a computernetwork; display logic configured to display the image in real time andto display image tags received from the computer network in response tothe image, the image tags characterizing contents of the image; and aprocessor configured to execute at least the display logic.

Various embodiments of the invention include an image processing systemcomprising an I/O configured to communicate an image sequence and imagetags over a communication network; optional an automatic identificationinterface configured to communicate the image sequence to an automaticidentification system and to receive a computer generated review of theimage from the automatic identification system, the computer generatedreview including one or more image tags identifying contents of theimage; destination logic configured to determine a first destination tosend the image sequence to, for a first manual review of the imagesequence by a first human reviewer; image posting logic configured topost the image sequence to the destination; review logic configured toreceive the a manual review of the image sequence from the destinationand optionally to receive the computer generated review, the manualreview including one or more image tags identifying an action within ofthe image sequence; response logic configured to provide the image tagsof the manual review to the communication network; memory configured tostore the image sequence; and a microprocessor configured to execute atleast the destination logic.

Various embodiments of the invention include a method of processing animage, the method comprising: receiving one or more first descriptors ofan image at an image processing server, from a remote client via acommunication network; comparing the received first descriptors tosecond descriptors stored locally to the image processing server, todetermine if the first descriptors match a set of the seconddescriptors; responsive to the first descriptors matching the set ofsecond descriptors, retrieving one or more image tags stored inassociation with the set of second descriptors; and providing the one ormore image tags to the client.

Various embodiments of the invention include a method of processing animage at an image processing server, the method comprising: receiving animage and data characterizing the image from a remote client;determining a destination for the image, the destination beingassociated with a human image reviewer, the determination of thedestination being based on a match between the data characterizing theimage and a specialty of the human reviewer; posting the image to thedetermined destination; receiving one or more image tags characterizingthe image, from the destination; and providing the one or more imagetags to the client.

Various embodiments of the invention include a method of processing animage, the method comprising: receiving data characterizing the imagefrom a mobile device, the data characterizing the image includingidentified features of an image or descriptors of an image; generatingimage tags based on the data characterizing the image; providing theimage tags to the mobile device.

Various embodiments of the invention include a method of processing animage, the method comprising: receiving an image using a portabledevice; identifying features of the image using a processor of theportable device; providing the features to a remote image processingserver via a communication network; receiving image tags based on thefeatures from the image processing server; and displaying the image tagson a display of the portable device.

Various embodiments of the invention include a method of processing animage, the method comprising: receiving an image using a portabledevice; identifying features of the image using a processor of theportable device; deriving image descriptors based on the identifiedfeatures; providing the descriptors to a remote image processing servervia a communication network; receiving image tags based on thedescriptors from the image processing server; and displaying the imagetags on a display of the portable device.

Various embodiments of the invention include a method of processing animage, the method comprising: receiving an image using a portabledevice; identifying features of the image using a processor of theportable device; deriving image descriptors based on the identifiedfeatures; comparing the image descriptors with a set of imagedescriptors previously stored on the portable device to determine ifthere is a match between the image descriptors and the stored set ofimage descriptors; if there is a match between the image descriptors andthe stored set of image descriptors retrieving one or more image tagsassociated with the set of image descriptors from memory of the portabledevice; displaying the retrieved one or more image tags on a display ofthe portable device.

Various embodiments of the invention include a method of processing animage, the method comprising: receiving an image using a portabledevice; identifying features of the image using a processor of theportable device; deriving image descriptors based on the identifiedfeatures; comparing the image descriptors with a set of imagedescriptors previously stored on the portable device to determine ifthere is a match between the image descriptors and the stored set ofimage descriptors; classifying the image based on the match between theimage descriptors and the stored set of image descriptors; sending theimage and the classification of the image to a remote image processingserver; receiving one or more image tags based on the image; anddisplaying the one or more image tags on a display of the portabledevice.

Various embodiments of the invention include an image processing systemcomprising an I/O configured to communicate an image and image tags overa communication network; an image ranker configured to determine apriority for tagging the image; destination logic configured todetermine a first destination to send the image to, for a first manualreview of the image by a first human reviewer; image posting logicconfigured to post the image to the destination; review logic configuredto receive the a manual review of the image from the destination, themanual review including one or more image tags identifying contents ofthe image; memory configured to store the one or more image tags in adata structure; and a microprocessor configured to execute at least theimage ranker.

Various embodiments of the invention include an image processing systemcomprising an I/O configured to receive an image over a communicationnetwork; an image ranker configured to determine a priority of the imageand to determine whether or not to tag the image based on the priorityand/or how to tag the image; manual or automatic means for tagging theimage to produce one or more image tags characterizing the image; memoryconfigured to store the image and the one or more image tagscharacterizing the image, in a data structure; and a microprocessorconfigured to execute at least the image ranker.

Various embodiments of the invention include an image processing systemcomprising an I/O configured to receive an image over a communicationnetwork; an image ranker configured to determine a priority of the imageand to select a process of tagging the image based on the priority;means for tagging the image to produce one or more image tagscharacterizing the image; memory configured to store the image and theone or more image tags characterizing the image, in a data structure;and a microprocessor configured to execute at least the image ranker.

Various embodiments of the invention include an image processing systemcomprising an I/O configured to communicate an image and image tags overa communication network; an image ranker configured to determine apriority for tagging the image based on how many times a video includingthe image is viewed; destination logic configured to determine adestination to send the image to, for a manual review of the image by ahuman reviewer; image posting logic configured to post the image to thedestination; review logic configured to receive the manual review of theimage from the destination, the manual review including one or moreimage tags identifying contents of the image; memory configured to storethe one or more image tags in a data structure; and a microprocessorconfigured to execute at least the image ranker.

Various embodiments of the invention include an method of processing animage, the method comprising receiving an image from an image source;distributing the image to an automated image identification system;receiving a computer generated review from the automated imageidentification system, the computer generated review including one ormore image tags assigned to the image by the automated imageidentification system and a measure of confidence, the measure ofconfidence being a measure of confidence that the image tags assigned tothe image correctly characterize contents of the image; assigning apriority to the image based on the measure of confidence; determiningthat the image should be manually tagged based on the priority; postingthe image for manual review to a first destination, the firstdestination including a display device of a human image reviewer; andreceiving a manual image review of the image from the destination, theimage review including one or more image tags assigned to the image bythe human image reviewer, the one or more image tags assigned by thehuman image reviewer characterizing contents of the image.

Various embodiments of the invention include an method of processing animage, the method comprising receiving an image from an image source;automatically determining a priority to the image using amicroprocessor; determining how the image should be tagged based on thepriority; tagging the image to produce one or more tags, the one or moretags characterizing contents of the image; and storing the image and theone or more tags in a data structure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an image processing system, according to variousembodiments of the invention.

FIG. 2 illustrates an image capture screen, according to variousembodiments of the invention.

FIG. 3 illustrates search results based on an image analysis, accordingto various embodiments of the invention.

FIG. 4 illustrates methods of processing an image, according to variousembodiments of the invention.

FIG. 5 illustrates alternative methods of processing an image, accordingto various embodiments of the invention.

FIG. 6 illustrates methods of managing a reviewer pool, according tovarious embodiments of the invention.

FIG. 7 illustrates methods of receiving image tags in real-time,according to various embodiments of the invention.

FIG. 8 illustrates methods of upgrading an image review, according tovarious embodiments of the invention.

FIG. 9 illustrates an example of Image Source 120A including electronicglasses, according to various embodiments of the invention.

FIG. 10 illustrates a method of processing an image on an image source,according to various embodiments of the invention.

FIG. 11 illustrates a method of processing an image based on imagedescriptors, according to various embodiments of the invention.

FIG. 12 illustrates a method of processing an image using feedback,according to various embodiments of the invention.

FIGS. 13 and 14 illustrates methods of providing image tags based onimage descriptors, according to various embodiments of the invention.

FIG. 15 illustrates methods of prioritizing image tagging, according tovarious embodiments of the invention.

FIG. 16 illustrates a method of performing a search based on an image,according to various embodiments of the invention.

DETAILED DESCRIPTION

FIG. 1 illustrates an Image Processing System 110, according to variousembodiments of the invention. Image Processing System 110 is configuredfor tagging of images and may include one or more distributed computingdevices. For example, Image Processing System 110 may include one ormore servers located at geographically different places. ImageProcessing System 110 is configured to communicate via a Network 115.Network 115 can include a wide variety of communication networks, suchas the internet and/or a cellular telephone system. Network 115 istypically configured to communicate data using standard protocols suchas IP/TCP, FTP, etc. The images processed by Image Processing System 110are received from Image Sources 120 (individually labeled 120A, 120B,etc.). Image Sources 120 can include computing resources connected tothe internet and/or personal mobile computing devices. For example ImageSource 120A may be a web server configured to provide a socialnetworking website or a photo sharing service. Image Source 120B may bea smart phone, camera, a wearable camera, electronic glasses or otherportable image capture device. Image sources may be identified by auniversal resource locator, an internet protocol address, a MAC address,a cellular telephone identifier, and/or the like. In some embodimentsImage Processing System 110 is configured to receive images from a largenumber of Image Sources 120.

Part of the image tagging performed by Image Processing System 110includes sending images to Destinations 125 (individually labeled 125A,125B, etc.). Destinations 125 are computing devices of human imagereviewers and are typically geographically remote from Image ProcessingSystem 110. Destinations 125 include at least a display and data entrydevices such as a touch screen, keyboard and/or microphone. For example,Destinations 125 may be in a different building, city, state and/orcountry than Image Processing System 110. Destinations 125 may includepersonal computers, computing tablets, smartphones, etc. In someembodiments, Destinations 125 include a (computing) applicationspecifically configured to facilitate review of images. This applicationis optionally provided to Destinations 125 from Image Processing System110. In some embodiments, Image Processing System 110 is configured forhuman image reviewers to log into a user account from Destinations 125.Destinations 125 are typically associated with an individual reviewerand may be identified by an internet protocol address, a MAC address, alogin session identifier, cellular telephone identifier, and/or thelike. In some embodiments, Destinations 125 include an audio to textconverter. Image tagging data provided by a human image reviewer at amember of Destinations 125 is sent to Image Processing System 110. Theimage tagging data can include textual image tags, audio data includingverbalized tags, and/or non-tag information such as upgrade requests orinappropriate (explicit) material designations.

Image Processing System 110 includes an I/O (input/output) 130configured for communicating with external systems. I/O 130 can includerouters, switches, modems, firewalls, and/or the like. I/O 130 isconfigured to receive images from Image Sources 120, to send the imagesto Destinations 125, to receive tagging data from Destinations 125, andoptionally to send image tags to Image Sources 120. I/O 130 includescommunication hardware and optionally an application program interface(API).

Image Processing System 110 further includes Memory 135. Memory 135includes hardware configured for the non-transient storage of data suchas images, image tags, computing instructions, and other data discussedherein. Memory 135 may include, for example, random access memory (RAM),hard drives, optical storage media, and/or the like. Memory 135 isconfigured to store specific data, as described herein, through the useof specific data structures, indexing, file structures, data accessroutines, security protocols, and/or the like.

Image Processing System 110 further includes at least one Processor 140.Processor 140 is a hardware device such as an electronic microprocessor.Processor 140 is configured to perform specific functions throughhardware, firmware or loading of software instructions into registers ofProcessor 140. Image Processing System 110 optionally includes aplurality of Processor 140. Processor 140 is configured to execute thevarious types of logic discussed herein.

Images received by Image Processing System 110 are first stored in anImage Queue 145. Image Queue 145 is an ordered list of images pendingreview, stored in a sorted list. Images stored in Image Queue 145 aretypically stored in association with image identifiers used to referencethe images and may have different priorities. For example, imagesreceived from a photo sharing website may have lower priority thanimages received from a smartphone. Generally, those images for which arequester is waiting to receive image tags representing an image inreal-time are given higher priority relative to those for which theimage tags are used for some other purpose. Image Queue 145 isoptionally stored in Memory 135.

Within Image Queue 145 images are optionally stored in association withan image identifier or index, and other data associated with each image.For example, an image may be associated with source data relating to oneof Image Sources 120. The source data can include geographic informationsuch as global positioning system coordinates, a street and/or cityname, a zip code, and/or the like. The source data may include aninternet protocol address, a universal resource locator, an accountname, an identifier of a smartphone, and/or the like. Source data canfurther include information about a language used on a member of ImageSources 120, a requested priority, a search request (e.g., an request todo an internet search based on image tags resulting from the image),and/or the like.

In some embodiments, an image within Image Queue 145 is stored inassociation with an indication of a particular subset of the image, thesubset typically including an item of particular interest. For example,a requestor of image tags may be interested in obtaining image tagsrelating to the contents of a particular subset of an image. This canoccur when an image includes several objects. To illustrate, consideringan image of a hand with a ring on one of the fingers, the user may wishto designate the ring as being a particular area of interest. Someembodiments of the invention include an application configured for auser to specify the particular item of interest by clicking on theobject or touching the object on a display of Image Source 120B. Thisspecification typically occurs prior to sending the image to ImageProcessing System 110.

If an image is stored in association with an indication that aparticular subset of the image is of particular importance, then anImage Marking Logic 147 is optionally used to place a mark on the image.The mark being disposed to highlight the particular subset. This markmay be made by modifying pixels of the image corresponding to the subsetand this mark allows a human image reviewer to focus on the markedsubset. For example, the image may be marked with a rectangle or circleprior to the image being posted to one or more of Destinations 125. Forexample, highlighting a subset of the image or an object within theimage can include applying a filter to the object or subset, and/orchanging a color of the object or subset. In alternative embodiments,Image Marking Logic 147 is included within an application configured toexecute on one or more of Image Sources 120 or Destinations 125. ImageMarking Logic 147 includes hardware, firmware, and/or software stored ona non-transient computer readable medium. As discussed elsewhere herein,Marking Logic 147 is optionally configured to place a mark on the imagein real-time, as the image is being generated.

In some embodiments, Marking Logic 147 is configured to use imagefeatures detected within an image to identify particular objects thatmay be marked. The detection of image feature is discussed elsewhereherein and is optionally part of image processing that occurs on theclient side, e.g., on Image Source 120A. For example, features such asedges may be detected using a processor of Image Source 120A. Thesefeatures can first be used in highlighting objects for detection andthen also sent from Image Source 120A to Image Processing System 110where they are then used to generate image descriptors as part ofprocessing the image. In this way automated processing of the image isdistributed between Image Source 120A, Image Processing System 110and/or Automatic Identification System 152.

Under the control of Processor 140, images within Image Queue 145 areprovided to an Automatic Identification Interface 150. The images areprovided thus as a function of their priority and position in ImageQueue 145. Automatic Identification interface 150 includes logicconfigured to communicate the image, and optionally any data associatedwith the image, to an Automatic Identification System 152. The logic ishardware, firmware, and/or software stored on a computer readablemedium. Automatic Identification Interface 150 is further configured toreceive a computer generated review of the image from AutomaticIdentification System 152, the computer generated review including oneor more image tags identifying contents of the image. In someembodiments, Automatic Identification Interface 150 is configured tocommunicate the image and data via Network 115 in a format appropriatefor an application programming interface (API) of AutomaticIdentification System 152. In some embodiments, Automatic IdentificationSystem 152 is included within Image Processing System 110 and AutomaticIdentification Interface 150 includes, for example, a system call withinan operating system or over a local area network.

Automatic Identification System 152 is a computer automated systemconfigured to review images without a need for human input on a perpicture basis. The output of Automatic Identification System 152 is acomputer generated image review (e.g., a review produced without humaninput on a per picture basis.) Rudimentary examples of such systems areknown in the art. See, for example, Kooaba, Clarifai, AlchemyAPI andCatchoom. Automatic Identification System 152 is typically configured toautomatically identify objects within a two dimensional image based onshapes, characters and/or patterns detected within the image. AutomaticIdentification System 152 is optionally configured to perform opticalcharacter recognition and/or barcode interpretation. In someembodiments, Automatic Identification System 152 is distinguished fromsystems of the prior art in that Automatic Identification System 152 isconfigured to provide a computer generated review that is based on theimage subset indication(s) and/or image source data, discussed elsewhereherein.

Automatic Identification System 152 is optionally configured todetermine if a copy of the image received from a different image sourcehas already been tagged. For example, the same image may be included inmultiple webpages. If the image is extracted from a first of thesewebpages and tagged, Automatic Identification System 152 may recognizethat the image has already been tagged and automatically assign thesetags to each instance of the image found. Recognizing that an image hasalready been tagged optionally includes comparing the image, a part ofthe image, or data representative of the image to a database ofpreviously tagged images. The image may have been previously taggedautomatically or manually.

In addition to one or more image tag(s), a computer generated reviewgenerated by Automatic Identification System 152 optionally includes ameasure of confidence representative of a confidence that the one ormore image tags correctly identify the contents of the image. Forexample, a computer generated review of an image that is primarilycharacters or easily recognizable shapes may have a greater confidencemeasure than a computer generated review of an image that consists ofabstract or ill-defined shapes. Different automated image recognitionsystems may produce different confidence levels for different types ofimages. Automatic Identification Interface 150 and AutomaticIdentification System 152 are optional in embodiments in which automaticidentification is performed by a third party.

Image Processing System 110 further includes a Reviewer Pool 155 andReviewer Logic 157 configured to manage the Reviewer Pool 155. ReviewerPool 155 includes a pool (e.g., group or set) of human image reviewers.Each of the human image reviewers is typically associated with adifferent member of Destinations 125. For example, each of the differentmembers of Destinations 125 may be known to be operated by a differenthuman image reviewer or to be logged into an account of a differenthuman image reviewer. Memory 135 is optionally configured to storeReviewer Pool 155. In some embodiments, the human image reviewersincluded in Reviewer Pool 155 are classified as “active” and “inactive.”For the purposes of this disclosure, an active human image reviewer isconsidered to be one that is currently providing image tags or hasindicated that they are prepared to provide image tags with minimaldelay. In embodiments that include both active and inactive human imagereviewers, the active reviewers are those that are provided image forreview. The number of active reviewers may be moderated in real-time inresponse to a demand for image reviews. For example, the classificationof a human image reviewer may be changed from inactive to active basedon a number of unviewed images in Image Queue 145. An inactive revieweris one that is not yet active, that has let the review of an imageexpire, and/or has indicated that they are not available to reviewimages. Inactive reviewers may request to become active reviewers.Inactive reviewers who have made such a request can be reclassified asactive human image reviewers when additional active human imagereviewers are needed. The determination of which inactive reviewers arereclassified as active reviewers is optionally dependent on a reviewerscore (discussed elsewhere herein).

Reviewer Logic 157 is configured to manage Reviewer Pool 155. Thismanagement optionally includes the classification of human imagereviewers as active or inactive. For example, Reviewer Logic 157 may beconfigured to monitor a time that a human image reviewer takes to reviewan image and, if a predetermined maximum review time (referred to hereinas an image expiration time), changing the classification of the humanimage reviewer from active to inactive. In another example, ReviewerLogic 157 may be configured to calculate a review score for a humanimage reviewer. In some embodiments, the review score is indicative ofthe completeness, speed and/or accuracy of image reviews performed bythe particular human image reviewer. The review score can be calculatedor changed based on review times and occasional test images. These testimages may be, for example images placed in Image Queue 145 that havebeen previously reviewed by a different human image reviewer. The reviewscore may also be a function of monetary costs associated with the humanimage reviewer. Reviewer Logic 157 includes hardware, firmware, and/orsoftware stored on a non-transient computer readable medium. In someembodiments, reviewer scores are manually determined by humanmoderators. These human moderators review images and the tags assignedto these images by human image reviewers. Moderators are optionally senta statistical sampling of reviewed images and they assign a score to thetagging of the images. This score is optionally used in determiningreviewer scores.

In some embodiments, Reviewer Logic 157 is configured to monitor statusof human image reviewers in real-time. For example, Reviewer Logic 157may be configured to monitor the entry of individual words or keystrokesas entered by a reviewer at Destination 125A. This monitoring can beused to determine which reviewers are actively reviewing images, whichreviewers have just completed review of an image, and/or which reviewershave not been providing tag input for a number of seconds or minutes.The entry of tag words using an audio device may also be monitored byReviewer Logic 157.

In some embodiments, members of Reviewer Pool 155 are associated with aspecialty in which the human image reviewer has expertise or specialknowledge in. For example, a reviewer may be an expert in automobilesand be associated with that specialty. Other specialties may includeart, plants, animals, electronics, music, food medical specialties,clothing, clothing accessories, collectables, etc. As is discussedelsewhere herein, a specialty of a reviewer may be used to select thatreviewer during an initial manual review and/or during a review upgrade.

The review score and/or specialty associated with a human image reviewerare optionally used by Reviewer Logic 157 to determine which inactivereviewer to make active, when additional active reviewers are required.Reviewer Logic 157 includes hardware, firmware, and/or software storedon a non-transient computer readable medium.

Image Processing System 110 further includes Destination Logic 160.Destination Logic 160 is configured to determine one or moredestinations (e.g., Destinations 125) to send an image to for manualreview. Each of Destinations 125 is associated with a respective humanimage reviewer of Reviewer Pool 155. The determinations made byDestination Logic 160 are optionally based on characteristics of thehuman image reviewer at the determined destination. The destination maybe a computing device, smartphone, tablet computer, personal computer,etc. of the human image reviewer. In some embodiments, the destinationis a browser from which the reviewer has logged into Image ProcessingSystem 110. In some embodiments, determining the destination includesdetermining an MAC address, session identifier, internet protocol and/oruniversal resource locator of one of Destinations 125. Destination Logic160 includes hardware, firmware and/or software stored on anon-transient computer readable medium.

Typically, Destination Logic 160 is configured to determine Destinations125 associated with active rather than inactive human image reviewers asdetermined by Reviewer Logic 157. Destination Logic 160 is alsotypically configured to determine Destinations 125 based on reviewscores of reviewers. For example, those reviewers having higher reviewerscores may be selected for higher priority reviews relative to reviewershaving lower reviewer scores. Thus, the determination of a member ofDestinations 125 can be based on both reviewer scores and image reviewpriority.

In some embodiments, Destination Logic 160 is configured to determineone or more members of Destinations 125 based on the real-timemonitoring of the associated reviewers' input activity. As discussedelsewhere herein, this monitoring may be performed by Reviewer Logic 157and can include detection of individual words or keystrokes entered by ahuman image reviewer. In some embodiments, Destination Logic 160 isconfigured to favor selecting Destination 125A at which a human imagereviewer has just completed a review of an image relative to Destination125B at which a human image reviewer is currently typing image tags on akeyboard.

In some embodiments, Destination Logic 160 is configured to use imagetags received via Automatic Identification System 152 to determine oneor more members of Destinations 125. For example, if an image tag of“car” is received via Automatic Identification Interface 150 thenDestination Logic 160 can use this information to select a member ofDestinations 125 associated with a human image reviewer that has aspecialty in automobiles.

The value of an image review may also be considered in the selection ofa destination for manual review. For example, an image review of highvalue may lead to the determination of a destination associated with ahuman image reviewer having a relatively high review score, while animage review of lower value may lead to the determination of adestination associated with a human image reviewer having a relativelylower review score. In some embodiments, for some image reviews,Destination Logic 160 is configured to select among Destinations 125 soas to minimize a time required to review an image, e.g., to minimize atime until the image tags of the manual review are provided to Network115.

Destination Logic 160 is optionally configured to determine multipledestinations for a single image. For example, a first destination may beselected and then, following an upgrade request, a second destinationmay be determined. The upgrade request may come from the Image Source120A or from a human image reviewer associated with the firstdestination. In some embodiments, Destination Logic 160 is configured todetermine multiple destinations, to which the image will be posted to inparallel. For example, two, three or more destinations, each associatedwith a different human image reviewer, may be determined and the sameimage posted to all determined destinations in parallel. As used in thiscontext, “in parallel” means that the image is posted to at least asecond destination before any part of a review is received from thefirst destination.

In various embodiments, there are a variety of reasons that two or moredestinations may be determined by Destination Logic 160. For example, arequest for an upgraded review may require a human image reviewer havinga particular specialty. Referring to the automotive example, an imagethat is first tagged with the tag “white car” may result in an upgraderequest for more information. Destination Logic 160 may be configured tothen select a destination associated with a human image reviewer have aspecialty in automobiles, e.g., a reviewer who can provide the tags“1976 Ford Granada.” An upgrade request indicates that the image issubject to further review, e.g. the image requires or may benefit fromfurther review. The upgrade request may be represented by a computingobject such as a flag, command or data value, etc.

Another instance that may require a second destination occurs when themanual review of an image takes too long. Typically, the tagging of animage should occur within an allotted time period or the review isconsidered to expire. The allotted time period is optionally a functionof the priority of the image review. Those reviews that are intended tooccur in real-time may have a shorter time period relative to lowerpriority reviews. If the review of an image expires, Image ProcessingSystem 110 is optionally configured to provide the image to anadditional human image reviewer associated with a destination determinedby Destination Logic 160.

Another instance that may require a second destination occurs when afirst human reviewer makes an upgrade request. For example, the requestto upgrade the review resulting in a tag of “car” may come from thehuman image reviewer that provided the tag “car.” While this example issimplistic, other examples may include images of more esoteric subjectmatter such as packaged integrated circuits.

Image Processing System 110 further includes Image Posting Logic 165configured to post images for manual review to Destinations 125determined by Destination Logic 160. Posting typically includescommunicating the images to one or more Destinations 125 via Network115. In various embodiments, Image Posting Logic 165 is furtherconfigured to provide information associated with the image toDestinations 125. For example, Image Posting Logic 165 may post, alongwith the image, an indication of a subset of the image (e.g., subsetidentification), an image marked by Image Marking Logic 147, informationidentifying a source of the image (e.g., source data discussed elsewhereherein), a priority of the review of the image, an image expirationperiod, location information associated with the image, and/or the like.As discussed elsewhere herein, source data can includes a universalresource locator, global positioning coordinates, longitude andlatitude, an account identifier, an internet protocol address, a socialaccount, an photo sharing account, and/or the like.

In some embodiments Image Posting Logic 165 is configured to provide animage for manual review to more than one of Destinations 125 at theapproximately the same time. For example, an image may be provided toDestination 125A and Destination 125B in parallel. “Parallel delivery”means, for example, that the image is provided to both Destinations 125Aand 125B before tagging information is received back from either ofthese Destinations 125.

In some embodiments, Image Posting Logic 165 is configured to provide animage for manual review to one or more of Destinations 125 prior toreceiving image tags from Automatic Identification System 152.Alternatively, in some embodiments, Image Posting Logic 165 isconfigured to wait until a computer generated review for the image isreceived from Automatic Identification System 152, prior to posting theimage to one or more of Destinations 125. In these embodiments, thecomputer generated review (including image tags) is optionally alsoposted to the one or more of Destinations 125 in association with theimage.

Image Posting Logic 165 is optionally configured to post identifiers ofimages along with the images. Image Posting Logic 165 includes hardware,firmware and/or software stored on a non-transient computer readablemedium.

Image Processing System 110 further includes Review Logic 170 configuredto manage the manual and automated reviews of images. This managementincludes monitoring progress of reviews, receiving reviews fromAutomatic Identification System 152 and/or Destinations 125. Thereceived reviews include image tags as discussed elsewhere herein. Insome embodiments, Review Logic 170 is configured to control posting ofthe image to one of Destinations 125 based on a measure of confidence.The measure of confidence being representative of a confidence that oneor more image tags already received are correct. These one or more imagetags may be received from Automatic Identification System 152 and/or oneof Destinations 125. For example, in some embodiments if the confidenceof an image review by Automatic Identification System 152 is greaterthan a predetermined threshold, then Review Logic 170 may determine thatmanual review of the image is not necessary. The predetermined thresholdcan be a function of the value of the image review, of the priority ofthe image review, of the number and quality of the availableDestinations 125, and/or the like. Review Logic 170 includes hardware,firmware, and/or software stored on a non-transient computer readablemedium.

In some embodiments, if an image was sent to Automatic IdentificationSystem 152 in parallel with being sent to one or more of Destinations125, then the receipt of a review from Automatic Identification System152 having a confidence above a predetermined threshold may result incancellation of the manual review at the one or more of Destinations 125by Review Logic 170. Likewise, if an image is sent to multipleDestinations 125 in parallel, and an image review is received from afirst of these Destinations 125, then Review Logic 170 is optionallyconfigured to cancel the review requests for the image at the otherDestinations 125. In some embodiments, Review Logic 170 is configured tocancel the review request at the other Destinations 125 once a keystrokeor word is received from the first of the Destinations 125.

In some embodiments Review Logic 170 is configured to monitor activityof a human image reviewer in real-time. This monitoring can includereceiving review inputs from Destinations 125 on a word by word orindividual keystroke basis. As discussed elsewhere herein, the wordsand/or keystrokes are optionally passed on to one of Image Sources 120as they are received by Review Logic 170. The monitoring of a manualreviewer's activity can be used to determine when the review of an imageexpires and/or the progress in completing a manual image review. Thestatus of a human image reviewer may be provided by Review Logic 170 toReviewer Logic 157 in real-time. Using this status, Reviewer Logic 157may change the status of the reviewer from active to inactive, adjust astored review score of the reviewer, establish or change a specialty forthe reviewer, and/or the like.

In some embodiments Review Logic 170 is configured to control posting ofimages to Destinations 125 by receiving measures of confidence (e.g., ofthe accuracy of image reviews) and sending responsive signals toDestination Logic 160 and/or Image Posting Logic 165. As such, ReviewLogic 170 can be configured to control posting of an image to one ormore of Destinations 125 based on a measure of confidence. The measureof confidence being representative of a confidence that one or moreimage tags correctly identify the contents of the image. In someembodiments, Review Logic 170 is configured to receive reviews frommanual image reviewers that include information other than image tags.For example, Review Logic 170 may receive an upgrade request from ahuman image reviewer and cause an upgraded image review to be requested.Review Logic 170 is optionally configured to process other non-taginformation received in a manual or computer generated review. Thisinformation can include identification of the image as being improper(e.g., obscene), identification of the image as containing noidentifiable objects, identification of the image as having been sent toa reviewer of the wrong specialty, and/or the like.

In some embodiments, Review Logic 170 is configured to adjust theconfidence of an image review by comparing image reviews of the sameimage from multiple sources. These image reviews may all be computergenerated, all be manual reviews, or include at least one computergenerated review and at least one manual review.

In some embodiments, Review Logic 170 is configured to provide imagetags received as part of a first (computer generated or manual) reviewand to provide the received image tags to a human image reviewer atDestinations 125B. An agent (e.g., a browser or special purposeapplication) executing on Destination 125B is optionally configured toprovide the image tags of the first review to a display of Destination125B. In this manner, the human image reviewer at Destination 125B canedit (add to, delete and/or replace) the image tags of the first review.For example, image tags received from Destination 125A may be providedto Destination 125B for modification.

In some embodiments, Review Logic 170 is configured to calculate reviewscores based on the results of image reviews received from Destinations125, the time taken for these image reviews, and the accuracy of theseimage reviews.

Various combinations of Automatic Identification Interface 150,Automatic Identification System 152, Destination Logic 160 and ImagePosting Logic 165 may be referred to herein as “image tagging logic.”Image tagging logic may include any combination of these elements. Oncean image has been associated with one or more tags characterizingcontents of the image (e.g., tagged), there are a wide variety of usesfor these tags. For example, the tags may just be sent back to the imagesource. The tags may also be used in more involved processes, such asretagging, selection of advertisements or product information,performing general searches, or performing searches on specific domains.

As used herein, the term “domain” is used to refer to an at leastpartially bounded set. Domains may include, for example, items havingspecific characteristics, items available from specific vendors, itemswithin specific geographic regions, items in a specific inventory, itemscharacterized by a specific classification scheme, items having specificphysical features, items classified by value, items classified by use orapplication, virtual or real items, or any other possible classificationor combinations thereof. Domains may likewise include services definedby one or more characteristic. Specific examples of domains include:insects, inventory available at a specific brick and mortar retaillocation, inventory from a specific vendor, jewelry, footwear, dresses,convertible cars costing over $50,000, restaurants in San Francisco, redhats, electronic books, medical equipment, video games, sportingequipment, backordered inventory, inventory available within Chicago,software, the internet, a specific web domain, etc. Image tagging logicincludes hardware, firmware and/or software stored on a non-volatilemedium.

Image tagging logic is optionally configured to determine a domain of animage. The domain may be determined based on contents of an image and/oron other information. For example, the image tags “dell computer” may beused to determine a domain of “computer,” the image tags “pink spotteddress” may be used to determine a domain of “woman's clothing,” and “theimage tags “Mona Lisa” may be used to determine a domain of “art.”

Image Processing System 110 optionally further includes a Token Ranker173. Token Ranker 173 is configured to parse image tags, such as thoseassociated with an image using image tagging logic, and to identify oneor more tokens within the image tags. These tokens may include, forexample, symbols, words, logos, characters, etc. Token Ranker 173 isoptionally disposed in Advertising System 180 or AutomaticIdentification System 152. Token Ranker 173 logic includes logic orcomputer code embodied in hardware, firmware and/or software stored on anon-volatile medium, and is optionally executed using Processor 140.

The determinations of domains may be performed by, for example,Automatic Identification System 152, Token Ranker 173 and/or a humanimage reviewer using Destination 125A. Image tags associated with animage by image tagging logic optionally include an indication (e.g.,metadata or order) indicating that one token within the tags has agreater relevance for domain identification than another token withinthe tags. For example, in the tags “Blue Car Flat Tire,” the token “Car”may be indicated as being most relevant to domain identification. Theindication can be accomplished by metadata or an order of tokens. Insome embodiments, a human reviewer is provided with an interface thatpermits prioritization or characterization of image tags entered by thehuman reviewer.

The determinations of domains may be based on the source of an image.For example, if an image is received from a specific mobile applicationbranded by a retail vendor, then the domain may be defined as theinventory available from that vendor. If the specific mobile applicationis executing on a smartphone within New York City, then the domain maybe further limited to inventory available from that vendor in New YorkCity. Likewise, if an image is received from Image Source 120A and ImageSource 120A is known to be used by a botanist then a default domain maybe determined to be “plants.” Likewise, if an image is received fromImage Source 120A located in a particular retail establishment, then thedomain may be determined to be the inventory of that retailer.

Token Ranker 173 is further configured to assign weightings (e.g.,numeric values) to tokens identified with image tags. These values areoptionally used rank or prioritize the tokens. In various embodimentsvalues are assigned based on importance to a specific domain, relevanceto a classification scheme, and/or uniqueness. As used herein, the term“uniqueness” is used to refer to a frequency at which a particular tokenis found within image tags, tokens that are found less often havinggreater uniqueness. In some embodiments, tokens having greateruniqueness are assigned greater weight. For example, the token “Dell”may have a lower weight than the token “D2509mx29” (a model number). Themodel number is given greater weight relative to the manufacturer name.The uniqueness of specific tokens is optionally calculated within aspecific domain. For example, in a domain of flatware from a specificvendor the token “fork” may have a lower weight as it is found morefrequently, relative to the token “lilac.” Note that “lilac” may be moreuseful in identifying a specific fork in an inventory than is “fork.”

In some embodiments, the uniqueness of a token, globally or within aspecific domain, is determined by examining image tags, optionallygenerated using Image Processing System 110. For example, images of aninventory available from a specific vendor may be tagged and theresulting tags analyzed to determine the uniqueness (frequency ofoccurrence) of specific tokens. In addition, the presence of tokensfound in previously established product names or product descriptions isoptionally considered in determining uniqueness of specific tokens. (Theproduct names and/or product descriptions may be used/considered intagging the images, as described elsewhere herein.) Global or domainspecific uniqueness data for specific tokens is optionally stored in adata structure configured to store such data in Memory 135. Tokenuniqueness and token weight are optionally domain specific. A token thatis not found at all in a specific domain is optionally given a minimal(e.g., zero) weight. The calculation of uniqueness is optionallyperformed using Token Ranker 173.

As an illustrative example of one embodiment, consider the image tags“Louis Viton clutch bag.” These tags include the tokens “Louis Viton,”“clutch” and “bag.” (“Louis Viton” is a phrase that may be considered asingle token because the words are typically found together.) For afirst vendor, the token “Louis Viton” may not be found at all in thetoken set of their domain. The vendor is optionally identified by alocation of or by a specific application executing on Image Source 120A.This token is, therefore, optionally given a weight of zero. The token“clutch” is found in 0.3% of the tags assigned to products within theirinventory (domain) and the token “bag” is found in 1.8% of the tagsassigned to products within their inventory. As a result the token“clutch” may be assigned a higher weight than the token “bag.” For asecond vendor, the token “Louis Viton” is found in 2% of the tagsassigned to products within their inventory, and the tokens “clutch” and“bag” are assigned to products in 1% and 4% of these tags. As a resultthe weights assigned to these tokens are (from greatest to lowest) inthe order “clutch,” “Louis Viton” and “bag.” In an alternative example,the token “bag” may be used to select a domain of bags (not necessarilyspecific to a given vendor) and the remaining tokens ranked within thisdomain based on their uniqueness in global usage (e.g., their frequencyof occurrence in the tags of all images).

In some embodiments, Token Ranker 173 is configured to identify groupsof tokens commonly found together in a phrase and treat then as a singletoken. Typically, this identification is based on a dataset of commonphrases, optionally stored in Memory 135. For example, in the aboveexample, “Louis Viton clutch bag.” Either “Louis Viton” or “clutch bag”may be considered a phrase, in various embodiments. Treating “clutchbag” as a single token has the advantage that, in search, a type ofpurse is not confused with an automotive clutch and an airbag. In someembodiments Token Ranker 173 is configured to add synonyms to a set ofimage tokens or to replace an image token with a synonym of that token.For example, “photograph” may be replaced by “image.” Synonyms areoptionally stored in Memory 135. Synonyms substation and phraseidentification is optionally accomplished by parsing the image tokensand filtering for commonly replaced tokens.

[In some embodiments Review Logic 170 is configured to provide imagereviews to a source of the image, e.g., one of Image Sources 120, usinga Response Logic 175. The image reviews may be provided when the imagereview is complete, on a character by character basis, or on a word byword basis. When provided on a character by character basis or a word byword basis, the image tags are optionally provided to the source of theimage as the characters or words are received from a human imagereviewer. Optionally Response Logic 175 is configured to provide theimage review via Network 115.

Image reviews are not necessarily returned to one of Image Sources 120.For example, if Image Source 120A is a photo sharing service or a socialnetworking website, image reviews of images from Image Source 120A maybe stored in association with an account on the photo sharing service orthe social networking website. This storage can be in Memory 135 or at alocation external to Image Processing System 110, such as at a webserverhosting the website. Image reviews are optionally both returned to oneof Image Sources 120 and stored elsewhere.

In some embodiments, Response Logic 175 is configured to execute asearch based on image tags received in a computer generated and/ormanual image review. The results of this search can be provided to asource of the image, e.g., Image Source 120A or 120B. For example, insome embodiments a user uses a smartphone to create an image with acamera of Image Source 120A. The image is provided to Image ProcessingSystem 110 which generates an image review of the image using AutomaticIdentification System 152 and Destination 125A (e.g., parts of imagetagging logic). The image review includes image tags that are thenautomatically used to perform an internet search (e.g., a google oryahoo search) on the image tags. The results of this internet search arethen provided to the user's smartphone.

In some embodiments, Image Processing System 110 includes a SearchEngine 177 configured to perform a search using image tags or tokensassociated with an image. In contrast with a generalized image search,Search Engine 177 is configured to perform searches in one or morespecific domains and optionally based on weightings of image tokens. Forexample, Search Engine 177 may be configured to search the inventory ofa specific product vendor. In this case, the products (or services)within the vendor are associated with or characterized by product tags.These tags may be generated from product names, product descriptionsand/or by tagging images of products using Image Processing System 110.Product tags are optionally weighted in a manner similar to theweighting of image tokens, discussed elsewhere herein. For example, aproduct tag including a specific Universal Product Code or model numbermay have a greater weight than a product tag of “black” or “electronic.”

Search Engine 117 is configured to optionally search a specific domainby matching image tokens generated by Token Ranker 173 to product tagsassociated with items (products or services) within the domain. Forexample, the tokens “short,” “purple” and “dress” may be matched withproducts having product tags “short length SAKS 5^(th) Ave. dress,backless” and “Dickeys purple dress.”

Search Engine 117 is optionally configured to report results of a searchbased on weighting of image tokens and/or weighting of product tags. Thematching of image tokens and/or product tags can include calculation ofa match quality that allows the products with best (or optionally worst)matches to be listed/provided first in the results. In the exampleabove, the tokens “short,” “purple” and “dress” may have a higher/bettermatch quality to “Dickeys purple dress” relative to “SAKS 5^(th) Ave.dress, backless” because the weight of the “purple” token is higher thanthe weight of the “short” token. As such, the product associated withthe product tags “Dickeys purple dress” will typically be presented inthe search results, and presented to a user of Image Source 120A beforethe product associated with the product tags “short length SAKS 5^(th)Ave. dress, backless.” In some embodiments, other factors, such as knownuser preferences, vendor sponsorship, paid listing priority, value ofgoogle Ad Words, etc. may result in alteration of this order. Further,product tags may also be weighted (generally or per specific domain). Inthese embodiments, the weighting of the product tags are used incombination with weighting of image tokens to calculate the matchquality. If no results are found in a particular search involvingmultiple image tokens, then the search is optionally repeated usingvarious subsets of the image tokens. Typically, it is possible to tryseveral different subsets for the multiple image tokens. The subsets areoptionally selected by removing either the highest or lowest weightedtokens from the search first, in an iterative process. If no results arefound in searches using all the possible subsets of a set of imagetokens, the number of domain(s) in which the search is performed isoptionally expanded.

Search Engine 177 includes logic or computing instructions embodied inhardware, firmware and/or software stored on a non-volatile computerreadable medium. Search Engine 177 is optionally disposed on AutomaticIdentification System 152, on Advertising System 180, on a vendorspecific computing device, and/or embodied in an application executingon Image Source 120A. Image Processing System 110 optionally includesmultiple instances of Search Engine 177, some of which are associatedwith specific vendors and/or domains. For example, a vendor of consumerelectronics may utilize an instance of Search Engine 177 disposed ontheir own computer servers and dedicated to their own inventory/domain,part of this Search Engine 177 may be included in an applicationconfigured to execute on Image Source 120A. Output of Search Engine 177,including ordered search results, are optionally provided to ImageSource 120A by Response Logic 175.

In some embodiments, a user of Image Source 120A has the option toupgrade the review of an image and/or a search based on search resultsgenerated using Search Engine 177. For example, a user may choose tohave the image further reviewed (as described elsewhere herein, e.g.,with regard to FIG. 4), and/or the user may manually add to the imagetokens to be searched. Specifically, in some embodiments of Image Source120A, a user interface is configured to display the search results to auser and for the user to refine the search, upgrade the image review,select/purchase a product within the search result, view productreviews, and/or the like. In some embodiments, the user is given anoption to upgrade an image review to an employee or representative of aspecific vendor. This employee or representative can then function as asalesperson or assistant to provide services to the user.

In some embodiments, Response Logic 175 is configured to provide imagetags of a computer generated and/or manual review to an AdvertisingSystem 180. Advertising System 180 is configured to selectadvertisements based on the image tags. The selected advertisements areoptionally provided to the source of the image used to generate theimage tags. For example, Response Logic 175 may provide the tags “1976Ford Granada with broken headlight” to Advertising System 180 and, inresponse, Advertising System 180 may select advertisements forreplacement headlights. If the source of the image used to generatethese tags is a website, the advertisements may be displayed on thewebsite. Specifically, if the source of the image is an account on aphoto sharing or social networking website, then the advertisements maybe displayed on that account. Advertising System 180 is optionallyincluded in Image Processing System 110. Advertising System 180 isoptionally configured to take bids for providing advertising in responseto specific tags. Advertising System 180 optionally includes Google'sAdwords.

Image Processing System 110 optionally further includes ContentProcessing Logic 185 configured to extract images for tagging frommembers of Image Sources 120. Content Processing Logic 185 is configuredto parse webpages including images and optionally text, and extractimages from these webpages for tagging. The resulting image tags maythen be provided to Advertising System 180 for selection ofadvertisements that can be placed on the webpage from which the imagewas extracted. In some embodiments, Content Processing Logic 185 isconfigured to emulate browser functions in order to load images thatwould normally be displayed on a webpage. These images may be displayedon a webpage associated with a specific account, a social networkingsite, a photo sharing site, a blogging site, a news site, a dating site,a sports site, and/or the like. Content Processing Logic 185 isoptionally configured to parse metadata tags in order to identifyimages.

Content Processing Logic 185 is optionally configured to parse textdisposed on the same webpage as an image. This text may be used byAutomatic Identification System 152 in tagging of the image, incombination with content of the image. For example, Content ProcessingLogic 185 may be configured to identify a caption for an image, commentsmade about an image, text referring to the image, webpage title orheadings, people or objects tagged within an image, text within an image(as determined by optical character recognition (OCR)), and/or the like.The text parsed by Content Processing Logic 185, or a subset thereof,may be used to improve quality and/or speed of tagging. The text parsedis provided to Automatic Identification System 152 and or provided toone of Destinations 125 for tagging by a human reviewer. In someembodiments Automatic Identification System 152 is configured to use theprovided text in the generation of tags for the image. For example, theprovided text may be used to provide context, identify a lexicon,ontology, language, and/or information that improves the accuracy,precision, computational efficiency, and/or other quality ofautomatically and/or manually generated image tags. The provided text istypically not relied on solely as a source of the generated tags, but isused as an input to improve the processing of the image. As such, theresulting tags may include words other than those found in the providedtext.

In some embodiments, Image Posting Logic 165 is configured to provideboth an image and text found on the same webpage as the image toDestinations 125. For example, an image of a girl and a bicycle at apark may have a caption “Mountain Bike Sale” or a comment “HappyBirthday Julie.” At Destination 125 this text may be presented to ahuman reviewer together with the image. The human reviewer may use thisinformation to better understand the focus and/or context of the image,and thereby provide better image tags. Likewise, in some embodiments,Automatic Identification Interface 150 is configured to provide both animage and text fund on the same webpage as the image to AutomaticIdentification System 152. At Automatic Identification System 152 theprovided text is used to improve the automated tagging of the imagebased on contents of the image. In the above example, the provided textmay suggest to Automatic Identification System 152 that emphasis shouldbe placed on the bike or on Julie. This may result in such widelydifferent tags as “Schwinn Bike” or “Birthday Girl.”

Image Processing System 110 optionally further includes an Image Ranker190. Image Ranker 190 is configured to determine a rank (e.g., priority)for tagging an image. The priority may be used to determine how or if atall to tag an image. The determination of priority may be based on, forexample, a source of the image, a number of times the image is loadedonto a webpage, a position of the image on a webpage, a number of timesthe image is viewed on a webpage, a number of webpages on which an imageincluded, a ranking of one or more webpage including the image, anidentity of a webpage including the image, a ranking of a second imageon the webpage including the image, an owner of webpage including theimage, a domain name of a webpage including the image, a keyword on awebpage including the image, text found on a webpage including theimage, metadata found on a webpage including the image, a number oftimes the image is clicked on the webpage, a number of times otherimages are clicked on the webpage, whether the image is part of a video,image tags automatically generated using Automatic Identification System152, any combination of these examples, and/or the like. Image Ranker190 includes logic in the form of hardware, firmware, and/or softwarestored on a computer readable medium. Image Ranker 190 includes logic inthe form of hardware, firmware, and/or software stored on a computerreadable medium. In various embodiments, the priority determined byImage Ranker 190 includes two levels (tag or no-tag), three levels(automatic tagging, manual tagging, or no-tag), ten priority levels, orsome other ranking scheme.

Destination Logic 160 is optionally configured to select a destinationof manual tagging of an image based on the priority of the image.

In those embodiments, wherein a number of times the image is loaded ontoa webpage is used to determine priority, the number may be per a fixedtime period such as per day or per month. The number can be determinedby including a line of Java or HTML script on the webpage, as is wellknown in the art. The position of the image on the webpage may beconsidered as some images may require that a viewer scroll down beforethe image is viewed. As such, the number of times the image is actuallyviewed may be used to calculate the image's priority. Typically, greaterpriority is assigned to images that are viewed more often. Image Ranker190 is optionally configured to assign priority to an image based on anumber of times the image is clicked on the webpage or on otherwebpages, and/or a number of times other images are clicked on thewebpage. Image Ranker 190 is optionally configured to determine prioritybased on a number of times an image is viewed on more than one webpage.For example, if the image is found on 25 different webpages, then thesum of the views on all the webpages may be used to determine priorityfor the image. In some embodiments Image Ranker 190 is configured todetermine priority based on a number of times an image is loaded in abrowser.

Popular images may be included in a number of webpages. For example animage that is widely shared on a social media website may be included onnumerous webpages. Image Ranker 190 may be configured to calculate thepriority of an image as a function of the number of webpages on which itis included and/or the number of webpages that include a link to theimage. Image Ranker 190 is optionally configured to identify an image asbeing included on multiple, possibly otherwise unrelated, webpages. Insome embodiments Image Ranker 190 is configured to use a third partyservice, such as TinEye.com, to determine the number of webpages onwhich an image is located. Typically, the greater the number of webpageson which an image is included, the greater the priority assigned to theimage.

In some embodiments, Image Ranker 190 is configured to calculate apriority of an image based on a ranking of one or more webpages thatinclude the image. For example, if a webpage is highly ranked in asearch engine, is linked to by a significant number of other webpages,or well ranked on some other criteria, then an image on the webpage maybe given a priority that is a function of the webpages' ranking.Typically the higher ranking a webpage has the greater priority isassigned to an image on the webpage. Webpage ranking is optionallyobtained from a third party source, such as a search engine.

Image Ranker 190 is optionally configured to assign a priority to animage based on an identity of a webpage including the image. Forexample, an image on a home page for a URL may be assigned greaterpriority than an image at another webpage for the same website. Further,images may be assigned a priority based on specific types of webpages onwhich the image is included. For example, images on social networkingwebsites may be given higher priority relative to images on companywebsites or personal blogs. In another example, images on referencewebpages such as dictionary.com or Wikipedia.com may be give higherpriority relative to some other types of webpages. The priority assignedto an image is optionally based on the identity of an owner of thewebpage.

In some embodiments, Image Ranker 190 is configured to determine apriority of a first age on a webpage as a function of the priority of asecond image on same webpage. For example, if the second image has ahigh priority the priority of the first image may be increasedaccordingly.

Image Ranker 190 is optionally configured to assign a priority to animage based on other contents of a webpage on which the image isincluded. For example, if the webpage includes text and/or metadata thepresence of specific terms or keywords in this text or metadata may beused to assign the priority of the image. Specifically, if a webpageincludes a valuable keyword then an image on that webpage may beassigned a higher priority. The estimated monetary value of a keyword isassociated with the value of the word for advertising or some otherpurpose, e.g., a word that has value on Google's Adwords®. An image on awebpage that includes terms that would be valued highly as Adwords maybe assigned a proportionally high priority. The frequency of use ofthese terms as well as their number on a webpage may also be consideredby Image Ranker 190 in determining image priority. The text and/ormetadata considered may be included in the URL of the webpage, within afigure caption, within a comment made on a figure, within a tag assignedto the image by a third party, near text referring to the image, aperson's name, a brand name, a trademark, a corporate name, and/or thelike.

In some embodiments, Image Ranker 190 is configured to receive textderived from an image using optical character recognition and todetermine a priority for the image based on this text. For example,Image Ranker 190 may receive text generated by processing an image usingAutomatic Identification system 152, and assign a priority to the imagebased on this text. In some embodiments, Image Ranker 190 is configuredto give a higher priority to a first image on a webpage, relative toimages that occur further down the webpage.

Image Ranker 190 is further configured to determine how, if at all, totag an image based on the assigned priority. For example, images oflowest priority may not be tagged at all. Images with somewhat higherpriority may be tagged using Automatic Identification System 152, andimage with yet higher priority may be tagged by a human reviewer at oneof Destinations 125. Those images having priority sufficiently high tobe tagged by a human reviewer are optionally further divided into higherand lower priority groups wherein images in the higher priority groupare given more attention and tagged more thoroughly or carefully by thehuman reviewer. Image Posting Logic 165 is optionally configured toprovide an indication of the priority of an image, along with the image,to members of Destinations 125.

In some embodiments, images are first processed using AutomaticIdentification System 152. Then the images may be sent to one or moremembers of Destinations 125 based on both a priority for the image and aconfidence in the automated tagging performed by AutomaticIdentification System 152. For example, if the image has relatively lowpriority then the confidence standard for sending the image to a humanreviewer is set relatively low. (A low confidence standard meaning thatthe automated tags are likely to be deemed sufficient and the image notsent for human analysis.) If the image has a relatively high prioritythen the confidence standard for sending the image to a human revieweris relatively higher. Thus, high priority images require a greaterconfidence to rely just on the automated tagging and are more likely tobe sent to a human reviewer.

The processing paths that may be selected by Image Ranker 190 for animage include, for example, a) not tagging at all, b) tagging using justAutomatic Identification System 152, c) tagging using AutomaticIdentification System 152 with optional human follow-up based on theimportance and/or confidence of the resulting tags, d) automated taggingfollowed by human review of the automated tags, e) tagging by a humanreviewer, and/or f) tagging by a human reviewer based on a suggestedlevel of attention to be given by the human reviewer. These processingpaths are, at least in part, selected based on the priority assigned tothe image by Image Ranker 190. Any combination of these processing pathsmay be found in various different embodiments. In some embodiments, theresult of controlling the type of processing used to tag an imageresults in those images that are potentially more valuable to have agreater probability of being tagged. As a result, the human taggingresources are applied to the highest priority—most valuable images.

In some embodiments, Image Ranger 190 is configured to assign a priorityfor an image based on how often an advertisement displayed adjacent toor over an image is clicked on. For example, if an image is on afrequently viewed webpage, but advertisements placed over the image arerarely clicked, then the image may be given a relatively high priorityfor tagging. In this example, an image may be tagged more than once. Ifadvertisements based on initial tags are not clicked on with an expectedfrequency, then the image may be retagged. Retagging is optionallyperformed by a human reviewer who receives, via Image Posting Logic 165,the image and the initial (inadequate) tags. The human reviewer can usethis information to provide improved tags.

FIG. 2 illustrates an Image Capture Screen 210, according to variousembodiments of the invention. Image Capture Screen 210 as illustrated isgenerated by, for example, an application executing on a smartphone,electronic glasses or other Image Source 120. Image Capture Screen 210is includes features configured to capture an image, mark a specificarea of interest, and receive image tags. Specifically, Image CaptureScreen 210 includes a Shutter Button 220 configured to take a picture.Once the picture is taken it is optionally automatically sent viaNetwork 115 to Image Processing System 110 for tagging. Image CaptureScreen 210 optionally further includes a Rectangle 230 configured tohighlight a point of interest within the image. Rectangle 230 iscontrollable (e.g., movable) by selecting and/or dragging on the screenusing a user input device. On a typical smartphone this user inputdevice may include a touch screen responsive to a finger touch. Asdescribed elsewhere herein, the point/region of interest may be providedto Image Processing System 110 in association with an image to betagged.

Image Capture Screen 210 further includes a Field 240 showing apreviously captured image and resulting image tags. In the example, showthe previously captured image includes the same white cup without theRectangle 230 and the image tags include “White Starbucks Coffee Cup.”Also shown is text stating “Slide for options.”

FIG. 3 illustrates search results based on an image analysis, accordingto various embodiments of the invention. These results are optionallydisplayed automatically or in response to selecting the “Slide foroptions” input shown in FIG. 2. They may be generated by automaticallyexecuting an internet search on the image tags. Illustrated in FIG. 3are a Sponsored Advertisement 310, Related Images 320 and other searchresults 330. The search results are optionally generated usingAdvertising System 180 and image tags generated using Image ProcessingSystem 110. A user may of the option of reviewing previously taggedimages. This history can be stored on Image Source 120A or in Memory135.

FIG. 4 illustrates methods of processing an image, according to variousembodiments of the invention. In these methods an image is received. Theimage is provided to both Automatic Identification System 152 and atleast one of Destinations 125. As a result, both computer generated andmanual image reviews are produced. The methods illustrated in FIG. 4 areoptionally performed using embodiments of the system illustrated inFIG. 1. The method steps illustrated in FIGS. 4-8 may be performed in avariety of alternative orders.

In a Receive Image Step 410 and image is received by Image ProcessingSystem 110. The image is optionally received from one of Image Sources120 via Network 115. The image may be in a standard format such as TIF,JPG, PNG, GIF, etc. The image may be one of a sequence of images thatform an image sequence of a video. The image may have been captured by auser using a camera. The image may have been captured by a user from amovie or television show. In some embodiments Receive Image Step 410includes a user using an image capture application to capture the imageand communicate the image to Image Processing System 110. Thisapplication may be disposed within a camera, television, video displaydevice, multimedia device, and/or the like. Receive Image Step 410 isoptionally facilitate using Content Processing Logic 185.

In one illustrative example, the image is received from image sequence,e.g. a video. The video is displayed on a monitor, television, goggle,glasses, or other display device. The video is optionally received via avideo streaming service such as youtube.com or Netflix.com® and/ordisplayed within a browser. Logic within the display system (e.g., ImageMarking Logic 147 within Image Source 120A) is configured for a user toindicate a particular subset of the images within the video. The samelogic may be configured to receive an advertisement selected in responseto image tags generated from the image and to display the advertisementover or at the same time as the video. Selection of advertisements basedon image tags is discussed further elsewhere herein.

Specifically, using this system, a user may select an object within avideo or movie for tagging and in response optionally receive tagscharacterizing that object. The user may also or alternatively receivean advertisement selected based on the tags. The advertisement may bedisplayed in real-time in conjunction with the video (e.g., as anoverlay or added video sequence) or provided to the user via othercommunication channels (e.g., e-mail). In one illustrative example, auser sees an object within a video that they like. They select theobject and this selection is received in Receive Image Step 410. Inresponse they receive an advertisement related to the object. Theadvertisement is displayed as an overlay, bar or caption on the video inreal-time as the video is viewed on the display. The advertisement isoptionally interactive in that it includes a link to make a purchase.

In some embodiments, objects within an image may include particularcharacteristics configured to assist in identifying the object. Forexample, a particular pattern of data bits may be encoded within theimage or within object of the image. These data bits may encode for animage tag.

In an optional Receive Subset Identification Step 415, data identifyingone or more subsets of the image is received by Image Processing System110. Typically, the one or more subsets include a set of image pixels inwhich an item of particular interest is located. The one or more subsetsmay be identified by pixel locations, screen coordinates, areas, and/orpoints on the received image. In some embodiments, the subsets areselected by a user using a touch screen or cursor of one of ImageSources 120.

In an optional Receive Source Data Step 420, source data regarding thesource of the image, received in Receive Image Step 410, is received byImage Processing System 110. As discussed elsewhere herein, the sourcedata can include geographic information, an internet protocol address, auniversal resource locator, an account name, an identifier of asmartphone, information about a language used on a member of ImageSources 120, a search request, user account information, and/or thelike. In some embodiments, source data is automatically sent by anapplication/agent running on Image Source 120. For example, globalpositioning system coordinates may automatically be generated on asmartphone and provided to Image Processing System 100.

In an optional Receive Analysis Priority Step 425 a priority for thetagging of the image, received in Receive Image Step 410, is receivedwithin Image Processing System 110. In some embodiments, the priority ismanually entered by a user of Image Source 120A. In some embodiments,the priority is dependent on an amount paid for the review of the image.In some embodiments, the priority is dependent on a type of ImageSources 120A. For example, images received from a static website mayautomatically be given a lower priority relative to images received froma handheld mobile device. An image whose source is identified by auniversal resource locator may be given a lower priority relative toimages whose source is identified by a mobile telephone number. As such,the priority is optionally derived from the source data received inReceive Source Data Step 420.

The image and data received in Steps 410-425 are optionally receivedtogether and optionally stored in Memory 135.

In a Distribute Image Step 430, the image, and optionally any associateddata received in Steps 415-425, is distributed to AutomaticIdentification System 152 via Automatic Identification Interface 150.This distribution may be internal to Image Processing System 110 or viaNetwork 115.

In a Receive Automated Response Step 435, a computer generated imagereview is received from Automatic Identification System 152. Thecomputer generated image review includes one or more image tags assignedto the image by Automatic Identification System 152. The computergenerated image review also includes a measure of confidence. Themeasure of confidence is a measure of confidence that the image tagsassigned to the image correctly characterize contents of the image. Forexample, an image including primarily easily recognizable characters mayreceive a higher measure of confidence relative to an image of abstractshapes.

In an Optional Determine Confidence Step 440, the measure of confidenceincluded in the image review is compared with one or more predeterminedlevels. The predetermined levels are optionally a function of thepriority of the image review, a price of the image review, a source ofthe image, and/or the like. In an Optional Confident? Step 445 theprocess proceeds to an optional Perform Search Step 450 if theconfidence of the computer generated image review is above thepredetermined threshold(s) and proceeds to a Queue Image Step 460 if theconfidence of the computer generated image is below the predeterminedthreshold(s). Determine Confidence Step 440 is optionally performedusing Review Logic 170.

In Perform Search Step 450, the image tags assigned to an image are usedto perform a search. For example, the image tag “Ford car” may be usedto automatically perform a google search using the words “Ford” and“car.”

In a Provide Results Step 455, the image tags assigned to the image andoptionally the results of a search performed in Perform Search Step 450are provided to a requester of the image review. For example, if theimage was received from Image Source 120A and Image Source 120A is asmartphone, then the image tags and search results are typicallyprovided to the smartphone. If the image was received from a member ofImage Sources 120, such as a website, that the image tags and optionalsearch results may be provided to a host of the website, to a thirdparty, to Advertising System 180, and/or the like. In some embodiments,the image tags are automatically added to the website such that theimage tags are searchable, e.g., can be searched on to find the reviewedimage.

In Queue Image Step 460, the image is placed in Image Queue 145. Thisplacement optionally includes marking a subset of the image using ImageMarking Logic 147. As described elsewhere herein, the marking istypically configured to identify objects of particular interest in theimage. Advancement of the image in Image Queue 145 may be dependent onthe image's review priority, the source of the image, available humanimage reviewers, the measure of confidence of the computer generatedreview of the image, and/or the like.

In a Determine Destination Step 465 one or more members of Destinations125 are determined for the manual review of the image. The determinationof a destination is optionally based on image tags included in acomputer generated image review received from Automatic IdentificationSystem 152; optionally based on specialties of human image reviewers atdifferent Destinations 120; optionally based on review scores of thesehuman image reviewers, and/or based on other criteria discussed herein.In some embodiments, Determine Destination Step 465 is based on the datacharacterizing the image and a specialty of the human reviewer. The datacharacterizing the image can be image features, image descriptors,and/or information derived therefrom. As is discussed elsewhere herein,the image features and/or image descriptors are optionally receivedalong with the image from a member of Image Sources 120. Informationderived therefrom may be generated at the member of Image Sources 120,at Image Processing System 110 and/or at Automatic Identification System152.

In a Post Image Step 470, the image is posted to at least one of theDestinations 125 determined in Determine Destination Step 465. In someembodiments, Post Image Step 470 includes posting the image to more thanone of Destinations 125 in parallel. The image is optionally posted viaNetwork 115 and is optionally posted along with a mark highlighting asubset of the image, source data for the image, a time before reviewexpiration for the image, image tags for the image received fromAutomatic Identification System 152, and/or the like.

In a Receive Review Step 475, a manual review of the image is receivedfrom one or more of the determined Destination(s) 125. The manual imagereview may include one or more image tags assigned to the image by ahuman image reviewer. The one or more image tags are representative ofthe content of the image. The manual review may also include an upgraderequest, an indication that the image is unreviewable, an indicationthat the image is improper, an indication that the review expired,and/or the like.

In an Image Tagged? Step 480 the progress of the method is dependent onwhether image tags were received in Receive Review Step 475. If imagetags characterizing the content of the image were received then themethod optionally proceeds with Perform Search Step 450 and ProvideResults Step 455. In these steps the image tags included in the manualimage review and optionally the computer generated image review areused. Use of the image tags in the computer generated image review maybe dependent on the confidence measure of this review.

Steps 460-475 are optional if in Step 445 the confidence measure isfound to be above the predetermined threshold(s).

In an optional Upgrade? Step 485 the progress of the method is dependenton whether an upgrade request has been received. If such a request hasbeen received then the method proceeds to Determine Destination Step 465wherein a second/different member of Destinations 125 is determined. Thedetermination may depend on image tags received in the manual imagereview received in Receive Review Step 475. The upgrade request may bereceived from a human image reviewer or from a requester of the imagereview (from Image Source 120A or 120B, etc.). The upgrade request maybe received after the requestor has had a chance to review the imagetags provided in Provide Results Step 455. For example, the requestormay first receive image tags consisting of “white car” and then requesta review upgrade because they desire further information. The reviewupgrade may result in the image being provided to a human image reviewerwith a specialty in automobiles. This human image review can add to theexisting image tags to produce “white car, 1976 Ford Granada.” In someembodiments, the requester can add source data indicating a subset ofthe image when requesting a review upgrade. For example, the reviewermay wish to indicate particular interest in a broken headlight. Thisserves to direct the human image reviewers attention to this feature ofthe image, produce tags that include “broken headlight,” and result in asearch (Perform Search Step 450), directed toward broken headlights fora 1976 Ford Granada.

In some embodiments, an upgrade request results in communication of animage to a third party image processing system. For example, if imagetags identify an article of clothing in an image, then an upgrade mayinclude sending the image to a (third party) system specializing inidentification of products related to fashion. Likewise, if image tagsidentify an insect, then an upgrade may include sending the image to a(third party) system specializing in insect identification.

In some embodiments, upgrade request are generate automatically byReview Logic 170. For example if an image review appears too brief,e.g., just “car,” then Review Lotic 170 may automatically initiate areview upgrade. In some embodiments, the automatic generation of upgraderequests is based on the presence of keywords within a manual imagereview. For example, certain review specialties are associated withlists of keywords. In some embodiments, when one of these keywords arereceived in a manual image review and an automated review upgrade isinitiated. The review upgrade preferably includes a human image reviewerhaving a specialty associated with the received keyword. In a specificexample, one specialty includes “automobiles” and is associated with thekeywords “car,” “truck,” “van,” “convertible,” and “Ford.” When one ofthese keywords is received in a manual image review, Review Logic 170checks with Review Logic 157 to determine if a human image reviewerhaving a specialty in “automobiles” is currently active. If so, then anautomatic upgrade is initiated and the image is sent to the Destination125B of the reviewer having the “automobiles” specialty.

If no upgrade requests are made, then in an End Step 490, the process iscompleted.

FIG. 5 illustrates alternative methods of processing an image, accordingto various embodiments of the invention. In these methods, at least someof Steps 430-445 are performed in parallel with at least some of Steps460-475. The manual image review is in Steps 460-475 may be begun beforethe computer generated review of Steps 430-445 is complete, thus, themanual image review is started before the confidence measure of thecomputer generated review is known. If, in Confident? Step 445, theconfidence measure is found to be above the predetermined threshold(s),then Steps 460-475 are optionally aborted.

FIG. 6 illustrates methods of managing a reviewer pool, according tovarious embodiments of the invention. In this method the status of areviewer may be changed based on their performance in reviewing images.The steps illustrated may be part of and performed in concert with themethods illustrated by FIGS. 4 and 5. For example, they may be performedin part between Receive Image Step 410 and Receive Review Step 475. Themethods illustrated include sending an image to more than one ofDestinations 125.

In Receive Image Step 410 an image is received. As is discussedelsewhere herein, the image may be received at Image Processing system110 via Network 115. The image may be generated by a camera and/orobtained from a webpage. In some embodiments, the image is receivedalong with information about how often the web page is viewed.

In a Select 1^(st) destination Step 610 a first destination is selectedfor manual or automated analysis of the image. Select 1^(st) DestinationStep 610 is performed using Destination Logic 160 and is an embodimentof Determine Destination Step 465. As described elsewhere herein, thedetermination of a destination for the image may be based on a widevariety of factors, including the status of a human reviewer and scoresassociated with reviewers. For example, typically a member ofDestinations 125 associated with an active reviewer will be selected,rather than one without an active reviewer. The selected destination maybe a member of Destinations 125 and/or Automatic Identification System152.

In Post Image Step 470, the image received in Receive Image Step 410 isposted to the selected member of Destinations 125. As discussedelsewhere herein, posting of the image can include communicating theimage via Network 115 using standard network protocols such as TCP orUDP.

In an optional Monitor Step 620, Reviewer Logic 170 is used to monitorprogress of a manual image review of the image at the member ofDestinations 125 selected in Select 1^(st) Destination Step 610. Themonitoring can include detection of input by a human reviewer, timetaken for the image review, a number of words provided that characterizethe image, and/or the like. Monitoring optionally includes measuring atime to taken to tag the image. Where monitoring includes detection ofinput by a human reviewer, the monitoring can be on akeystroke-by-keystroke basis, on a word-by-word basis and/or on aline-by-line basis. As such, Reviewer Logic 170 may be configured toreceive data characterizing the image a character, word or line at atime.

In Remove Step 630, the image is removed from processing at the memberof Destinations 125 Selected in Select 1^(st) Destination Step 610.“Removal” can include, notifying the human reviewer at the selectedmember of Destinations 125 that he or she is no longer primarilyresponsible for reviewing the image, relieving the human reviewer ofprimary responsibility (without necessarily notifying the humanreviewer), removing the image from a display of the human reviewer,and/or the like. In some embodiments, Remove Step 630 includes merelyplacing a human reviewer in a ranking to have secondary or sharedresponsibility for reviewing an image. For example, if the humanreviewer associated with the member of Destinations 125 selected inSelect 1^(st) Destination Step 610 had primary responsibility forreviewing an image, the responsibility may now be shared or assigned toother reviewers associated with other members of Destinations 125. Inthis case it is the primary responsibility that is “removed.”

Remove Step 630 may occur if manual review of the image is taking toolong. For example, if in Monitor Step 620 it is found that the reviewerhas not started typing after a predetermined time, then Remove Step 630may be performed. Other examples, of triggering events for Remove Step630 include, loss of communication with the selected member ofDestinations 125, exceeding a predetermined time allotment for review ofthe image, improper or inappropriate image tags received from the humanreviewer, inaccurate (not characterizing the image) image tags receivedfrom the human reviewer, a referral from a first human reviewer to asecond human reviewer, an upgrade request of the image review, and/orthe like.

In a Select 2^(nd) Destination Step 640 a second member of Destinations125 (or Automatic Identification System 152) is selected usingDestination Logic 160. The second member may be selected based on any ofthe criteria discussed above with regard to Select 1^(st) DestinationStep 610 and Determine Destination Step 465. Further, in someembodiments the selection of a second member may be based on a specificreferral by a human reviewer associated with the first member ofDestinations 125. For example, a first human reviewer may identify thecontent of an image to be a specialty of a second human reviewer and mayrefer the image to the member of Destinations 125 associated with thesecond human reviewer. The selection of a second member of Destinations125 in Select 2^(nd) Destination Step 640 is optionally dependent onautomated processing on an image using Automatic Identification System152.

In another Post Image Step 470 the image is posted to the member ofDestinations 125 Selected in Select 2^(nd) Destination Step 640. In someembodiments, more than one human reviewer may review an image inparallel. They may perform the review independently or in cooperation.One reviewer may have primary responsibility for review of the image oreach reviewer may have equal responsibility. One reviewer may havesupervisory responsibility over one or more other reviewers. In someembodiments, Select 2^(nd) Destination step 640 is performed and theimage is posted to two or more members of Destinations 125 prior toMonitor Step 620 and/or Remove Step 630.

In Receive Review Step 475 a review of the image, e.g., image tags, isreceived as discussed elsewhere herein. The review typically includesimage tags characterizing contents of the image. Reviews may be receivedfrom more than one of Destinations 125. For example, tags characterizingan image may be received from the members of Destinations 125 selectedin both Select 1^(st) Destination Step 610 and Select 2^(nd) DestinationStep 640. Receive Review Step 475 is optionally performed in real-timeas characters or words are provided by human reviewer(s).

In an optional Associate Tags Step 650, one or more image tagscharacterizing the image are stored in association with the image. Thestored tags optionally include tags provided by more than one humanreviewer and may be stored in Memory 135. As described elsewhere herein,the tags may further be provided to a member of Image Sources 120 (e.g.,in an embodiment of Provide Results Step 455) or used to selectadvertisements using Advertising System 180. The tags may also beprovided to Automatic Identification System 152 to provide training ofautomatic image recognition processes.

FIG. 7 illustrates methods of receiving image tags in real-time,according to various embodiments of the invention. These methods areoptionally performed by Image Processing System 110 and the image tagsmay be a result of a manual image review. These methods may be performedin concert with the other methods described herein, for example as partof the methods illustrated by FIG. 4. The methods begin with Post ImageStep 470 in which an image is provided to one or more members ofDestinations 125, as discussed elsewhere herein. The methods illustratedin FIG. 7 are optionally preceded by Receive Image Step 410 in which animage is received from a remote computing device.

In a Receive Input Step 710 input is received from the one or moremembers of Destinations 125. This input typically includes charactersprovided by a human reviewer. For example, the input may be characterstyped by a human reviewer at Destination 125A. Typically, Receive ImageStep 710 is continued as other steps shown in FIG. 7 are performed.

In a Detect 1^(st) Word Step 720 a word is detected in the inputreceived in Receive Input Step 710. The word may be detected by thepresence of a whitespace character such as an ASCII space or carriagereturn. Spell checking is optionally performed on the detected word. Ifthe word is not include in a spellcheck dictionary, then an attempt atcorrection may be made or the human reviewer may be notified of thefailure to recognize the word.

Detection of the word in Detect 1^(st) Word Step 720 results inexecution of a Deliver 1^(st) Word Step 730 in which the word iscommunicated to a source of the image. For example, once a word isdetected it may be provided to Image Source 120A in real-time. At ImageSource 120A the word can be displayed to a user. Displaying one word ata time can provide an impression that the analysis of the image isoccurring in a shorter amount of time, as compared to waiting until anentire set of image tags are received before displaying the set.

In a Detect 2^(nd) Word Step 740 a second word is detected in the inputreceived in Receive Input Step 710. Again, the word may be detected bythe presence of a whitespace character and can occur after providing thefirst word to the user at Image Source 120A. Both the first and secondwords are expected to be tags characterizing the image. Detection of thesecond word in Detect 2^(nd) Word Step 740 triggers a Deliver 2^(nd)Word Step 750 in which the second word is delivered to the image source,e.g., Image Source 120A. Detect 2^(nd) Word Step 740 and Deliver 2^(nd)Word 750 may be repeated for third, fourth and additional word, eachbeing part of the image tags.

In a Detect Completion Step 760 data indicating that processing of theimage is completed, e.g., that the words detected comprise all the words(image tags) to be provided by the human reviewer are received. The datamay include a metadata tag such as “/endtags,” an ASCII carriage return,and/or the like. Typically, Detect Completion Step 760 occurs after one,two or more image tags have been received. In optional Associate TagsStep 650 the received image tags are associated and/or stored with theimage as discussed elsewhere herein.

While FIG. 7 illustrates detection and delivery of a word at a time, inalternative embodiments, individual keystrokes are detected anddelivered. Receive Input Step 710 may continue in parallel with Steps720-740 and/or 750. Steps 710-760 may be include as part of ReceiveReview Step 475, discussed elsewhere herein.

FIG. 8 illustrates methods of upgrading an image review, according tovarious embodiments of the invention. In these methods an image receivesmore than one phase of image review. Following a first review (phase 1)the image review is upgraded and reviewed further (phase 2). The requestfor upgrade can be automatically generated, initiated by a first humanreviewer, and/or be in response to a request from a source of the image.Both the first and second review may be manual, i.e., performed by ahuman reviewer. Alternatively, the first review may be automatic and oneor more subsequent reviews may be manual, or the first review may bemanual and one or more subsequent reviews automatic.

In Receive Image Step 410 an image is received. A first member ofDestinations 125 is selected for the image in Select 1^(st) DestinationStep 610. The image is then posted in Post Image Step 470. These stepsare discussed elsewhere herein.

In a Receive 1^(st) Review Step 810 a first review of the image isreceived. This first review may include one or more image tagscharacterizing the contents of the image. For example, the image reviewmay include words “black spider” in response to a picture including animage of a black spider; or the image may include the words “red car” inresponse to an image including a red automobile. Receive 1^(st) ReviewStep is optionally an embodiment of Receive Review Step 475, and mayinclude the real-time communication of image tags as discussed in regardto FIG. 7.

In some embodiments, the first review can include an indication,provided by the human reviewer that performed the first image review,that the processing of the image should be upgraded. For example, thefirst human reviewer may manually indicate a field of expertise for asecond (optionally specialized) human reviewer. For example, a firsthuman reviewer may provide the “red car” image tags and suggest anupgraded review be performed by a reviewer with automotive expertise.Alternatively, the first review can include image tags that areconsidered particularly valuable. For example, an automatic review thatindicates a 72% probability that the image includes a wedding dress maytrigger an automated upgrade to a manual review because the image tags“wedding dress” are potentially of greater commercial value than otherimage tags. In some embodiments, this automated upgrade is performed byReview Logic 170 and is based on a list if relatively important orvaluable keywords stored in Memory 135. This list can include keywordsand an associated measure of their value. As discussed elsewhere herein,automatic upgrades performed by Review Logic 170 are optionally based onimage tags automatically generated using Automatic Identification System152 and/or information predictive of how often an image will be viewed.These factors are optionally applied using an algorithm that maximizesthe potential value of tagging the image using a human reviewer andproviding advertisements based on these tags. Examples of more valuableimage tags may be related to shoes, cars, jewelry, travel destinations,books, games, clothing, holidays, food, drink, real estate, banks,accidents, etc.

In some embodiments, upgrades of image reviews are automatic. Forexample, a tag of “black spider” may automatically result in an upgradeof the image review that includes sending the image to a human reviewerhaving a particular specialty, e.g., a spider expert. The identificationof particular plant or animal life often includes (depends on) locationinformation, as the location of the plant or animal can be important forproper identification.

In some embodiments, as discussed elsewhere herein, upgraded reviews maybe requested by the person who originally requested that the image bereviewed. For example, a user of Image Source 120A may provide an imageof a dog and received image tags comprising “black dog.” The user maythen request further detail by providing the word “breed?” In this casethe image review may be upgraded and sent to a human reviewer withspecific knowledge of dog breeds. In some embodiments, the user ischarged for the upgrade or is required to have a premium account inorder to request upgrades. The user may specify a particular part of animage when requesting an image review upgrade.

The presence of an upgrade request (automatically and/or manuallygenerated) is detected in a Detect Upgrade Request Step 820. Thedetection may be based on data or a command received from a member ofImage Sources 120, from a member of Destinations 125, AutomaticIdentification Interface 150, and/or from a component of ImageProcessing System 110 such as Review Logic 170, Content Processing Logic185 or Response Logic 175.

In a Select 2^(nd) Destination Step 640, Destination Logic 160 is usedto select a second member of Destinations 125 and/or AutomatedIdentification System 152 for review of the image. This selection can bebased on any of the criteria on which Select 1^(st) Destination Step 610was based and, in addition, the image tags and/or other informationresulting from the first review. For example, the selection of a secondmember of Destinations 125 may be based, at least in part, on an imagetag manually or automatically generated in the first image review.Specifically, a tag of “black spider” may be used by Destination Logic160 to select a member of Destinations 125 associated with a humanreviewer having expertise in the identification of spiders. In anotherexample, selection of a second member of Destinations 125 may be basedon a word provided by a user requesting the image review. Specifically,if the first review produces image tags “white shoe” and the userresponds with “brand?” then Destination Logic 160 may use thisinformation to select a member of Destinations 125 associated with ahuman reviewer having expertise in shoe brands.

In some embodiments of Select 2^(nd) Destination Step 640, DestinationLogic 160 is configured to possibly select Automatic IdentificationSystem 152 for the second review of the image, rather than a member ofDestinations 125. This may occur, for example, when the image has beentagged with the name of an actor and the upgrade request requests “moviename?” In such a case, the image may be searched for in a library ofmove images. The same approach may be taken for other reproducibleobjects such as currency, paintings, car models, trademarks, barcodes,QR codes, well known persons, etc.

In another instance of Post Image Step 470 the image is posted to thesecond selected member of Destinations 125 or Automatic IdentificationSystem 152 for a second review of the image. In a Receive 2^(nd) ReviewStep 830, image tags characterizing the content of the image aretypically received. Receive 2^(nd) Review Step 830 is optionally anembodiment of Receive Review Step 475. Alternatively, an additionalreferral, indication that the image cannot be tagged for some reason orother information may be received. The image tags are received from themember of Destinations 125 or Automatic Identification System 152 towhich the image was posted. Steps 820, 640, 470 and 830 may be repeatedif needed.

In Associate Tags Step 650 received image tags are associated with theimage and/or provided to the source of the image, as discussed elsewhereherein.

In one illustrative example of the methods illustrated by FIG. 8 animage is received from a web page. The image is sent to AutomaticIdentification System 152 for automated review. The result of theautomated review includes an image tag “ring.” This tag is processedusing Review Logic 170 and identified as a potentially valuable imagefor use in advertising. As discussed elsewhere herein, thisidentification is optionally also based on other factors such as howoften the image is viewed on the web page. As a result of theidentification, the review of the image is automatically upgraded andsent to a member of Destinations 125 that is associated with a humanreviewer having expertise in jewelry. The human reviewer modifies theimage tags to include “gold wedding ring” and these tags are associatedwith the image. These image tags may then be used to selectadvertisements using the systems and methods described elsewhere herein.

In one illustrative example of the method illustrated by FIG. 8 an imageis received from an application executing on a mobile device. The imageincludes a scene of a street and is sent to Destination 125A for reviewby a human reviewer. The human reviewer response with image tags “streetscene” and these tags are provided to the mobile device. A request for areview upgrade is then received from the mobile device. This requestincludes the text “car model?” and an identification of part of theimage including a car. As a result of this request the image, text andidentification are sent to Destination 125A or another member ofDestinations 125 for further manual review. The further review resultsin the image tags “1909 Model-T” which are then forwarded to the mobiledevice.

In one illustrative example of the method illustrated by FIG. 8 an imageis received from a computing device. The image includes several piecesof paper currency laying on a plate and is sent to Destination 125A formanual review. The resulting tags include “US currency on white plate”and are sent to the computing device. A request for an image reviewupgrade is received. The request includes “how much?” As a result ofthis request the image is sent to Automatic Identification System 152where automated currency identification logic is used to identify theamounts of the currency and optionally provides a sum. This informationis then sent back to the computing device.

In one illustrative example of the method illustrated by FIG. 8 an imageis received from a mobile device. The image includes a leaf of a plantand is sent to Destination 125A. The human reviewer at Destination 125Aprovides the image tags “green leaf” and also upgrades the image review.As a result of the upgrade and the image tags the image is sent toDestination 125B, which is associated with a second human image reviewerhaving expertise in botany. The selection of Destination 125B is based,in part on the image tags “green leaf.” In parallel, the image tags“green leaf” are sent to the mobile device. At Destination 125B thesecond human image reviewer adds the words (tags) “poison ivy” to thealready existing image tags. These additional tags are then also sent tothe mobile device. On the mobile device the words “green leaf” are firstdisplayed and then the words “poison ivy” are added to the display onceavailable.

The methods illustrated by FIG. 8 are optionally used in concert withother methods described herein. For example, the image tags may be usedin Auction Tag Step 1560 described elsewhere herein. The tags may beused to select an advertisement and the advertisement provided to aremote browser for display on a webpage along with the image.

FIG. 9 illustrates an example of Image Source 120A including electronicglasses, according to various embodiments of the invention. Electronicglasses include glasses to be worn by a person. Examples include “GoogleGlass” by Google®, “M100 Smart Glasses” by Vuzix®, the iOptik™ contactlens by Innovega, and/or the like. These systems are configured to allowa user to view both the real world and an electronic display at the sametime. Electronic glasses may also include virtual reality systems suchas the Oculus Rift™ by Oculus VR. These types of systems display imagesto a user using an electronic display, but do not provide simultaneousdirect view of the real world. A direct view is a view that is notdigitized for viewing, e.g., a view through a glass or lens.

Generally, electronic glasses provide in interactive interface in whicha user can select a subset of the image in real-time as the image isbeing viewed in or through the electronic glasses. As used herein,“real-time” selection is meant to mean that the image is being viewed asit is being captured, with only inconsequential delay. For example, animage viewed in real-time may be captured by a camera and processedusing a graphics engine and displayed with only a delay resulting fromelectronic processing times. Real-time viewing allows the user toposition objects of interest within the viewed image by moving the imagecapture device as the image is being viewed. Thus, real time viewingexcludes viewing of images that have been stored for substantial periodsbefore viewing.

As illustrated in FIG. 9, Image Source 120A includes a Camera 910configured to capture images. The captured images can include stillimages or video comprising a sequence of images. A Display 920 isconfigured to present the captured images to a user of Camera 910. Insome embodiments, such as those in which Image Source 120A is a smartphone, Display 920 includes a touch screen configured to function as aview finder for Camera 910, to display captured images and to displayImage Capture Screen 210 (described elsewhere herein). Display Logic 925is configured to manage the display of images and other content onDisplay 920. Display Logic 925 can include hardware, firmware and orsoftware stored on a computer readable medium

In the embodiments illustrated by FIG. 9, Image Source 120A furthercomprises Selection Logic 930 configured for the user of Image Source120 to indicate a subset of a captured image. This indication can bemade within Image Capture Screen 210 in real-time as the image is beingdisplayed and/or captured on Display 920. As discussed elsewhere herein,such an indication is made to select a particular object of interestwithin the image. Following selection, the subset of the image isoptionally marked using Image Marking Logic 147 as discussed elsewhereherein. Image Marking Logic 147 may be used add a mark to the image asshown within Display 920. As such, the user can see the location thathas been marked. Selection Logic 930 is optionally configured for theuser to remark the subset of the image until the user is satisfied withthe selection.

In some embodiments, Selection Logic 930 includes Tracking Logic 935configured to track movement of the user's eyes. Tracking Logic 935 isoptionally included within electronic glasses. Eye tracking can includedetection of the focal point of both eyes, the direction of one or moreeyes (eyeball direction), the focus of one or more eyes, blinking,eyeball movement, and/or the like. Tracking Logic 935 is optionallyconfigured to correlate a state of the user's eyes with a locationwithin captured images. Geometry data representing a geometric relationbetween Camera 910 and the physical elements of Selection Logic 930 areused to associate the state of the user's eyes with the location withinan image captured using Camera 910.

Tracking Logic 935 optionally includes a second camera directed at theeyes of the user. This camera may be mounted on electronic glasses or bepart of other embodiments of Image Sources 120. For example, TrackingLogic 935 configured for tracking a user's eyes may be included in a webcamera, a smartphone, a computer monitor, a television, a tabletcomputer, and/or the like.

In some embodiments, Tracking Logic 935 is configured to detect blinkingof one or more eyes. For example, Tracking logic 935 may be configuredto detect a single eye blink or a pattern of eye blinks. When such anevent is detected, Selection Logic 930 may select a position within animage based on eye position data received from Tracking Logic 935, oralternatively select a position at the center of the currently viewedimage.

Once an image has been marked using Marking Logic 147 and SelectionLogic 930, the location and/or area of the marking can be displayed tothe user on the marked image within Display 920. For example, the image,plus a red “X” at the marked location, may be displayed to the userwithin Image Capture Screen 210. In some embodiments, the user may thenconfirm the selection using Confirmation Logic 940. Confirmation Logic940 is optionally responsive to Tracking Logic 935. For example,confirmation may be provided using a blink or other eye movement, anaudio command, a verbal command, or a touch command. In someembodiments, Tracking Logic 935 is configured to detect, and interpretas a command, movement of the eyes into an unnatural position (e.g.,cross-eyed). Such a movement can be used to provide a confirmationcommand. Confirmation is optionally required prior to sending the imageto Network 115.

In some embodiments, Selection Logic 930 includes Tracking Logic 935that is configured to track something other than or in addition to theeyes. For example, Tracking Logic 335 may be configured to detect apointing finger of a user, an electronic device worn on a finger orwrist, and or the like. In these embodiments, Selection Logic 930 isconfigured to infer a location within an image based on the detectedobject. In one embodiment, Tracking Logic 935 is configured to detectthe location of a pointing finger within an image and infer that thelocation to be selected is at the tip of the finger. A user can point toan object within their field of view, provide an audio, eye based,and/or touch based command to Image Source 120A, and the position of thepointing finger will be used to make the selection of a position withinthe image. In one embodiment, Tracking Logic 935 is configured to detectthe location of a wireless electronic device relative to Image Source120A and infer that the location to be selected is along a line betweenthe wireless electronic device and a part of Image Source 120A.

Image Source 120A further includes an I/O 945 configured for ImageSource 120A to communicate to Image Processing System 110 via Network115. I/O 945 can include wired and/or wireless connections. For example,in some embodiments, I/O is configured to communicate wirelessly fromelectronic glasses to a cellular phone using a Bluetooth™ connection andthen for the communication to be forwarded from the cellular phone toNetwork 115 using Wifi or a cellular service.

Image Source 120 further includes an embodiment of Memory 135 configuredto store images captured using Camera 910, geometric data, account data,and/or the like. Memory 135 includes non-transient memory such as RandomAccess Memory (RAM) or Read Only Memory (ROM). Memory 135 typicallyincludes data structures configured to store captured images and markinglocations within these images.

Image Source 120 further includes a Processor 950. Processor 950 is adigital processor configured to execute computing instructions. Forexample, in some embodiments Processor 950 is encoded with computinginstructions to execute Display Logic 925, Selection Logic 930, ImageMarking Logic 147 and/or Tracking Logic 935. Processor 950 optionallyincludes an Application Specific Integrated Circuit (ASIC) orProgrammable Logic Array. (PLA).

Image Source 120 optionally further includes Object Tracking Logic 955.Object Tracking Logic 955 is configured to track movement of an objectof interest within a sequence of images. For example, in someembodiments, a user may use Selection Logic 930 to select a subset of animage or an aspect of the image for which information is requested. Thissubset may include one or more pixels. Object Tracking logic 955 isconfigured to use automatic (computer based) image interpretation logicto identify a specific object occupying the selected subset. This objectmay be a person, a vehicle, an animal, or any other object. Theboundaries or other pixels of the selected object are optionallyhighlighted in Display 920 by Object Tracking Logic 955. This highlightcan track the object as it moves within the sequence of images and caninclude changing pixel characteristics. The highlighting optionallymoves with the object on the display. An aspect of the image may be abrand of an object within the image, a movie from which the image isobtained, a location of the content of an image, etc. In someembodiments, aspects of the images can be specified as being of interestusing text such as “shoe brand?,” “movie?,” “actor?,” “location,”“breed?,” etc. Such specifications may be provided in an originalrequest to tag an image and/or in an upgrade request.

In some embodiments, images communicated from Image Sources 120 to ImageProcessing System 100 are part of a sequence of images that comprise ashort video sequence. These video sequences may be tagged using thesystems and methods described elsewhere herein. One advantage of tagginga video sequence is that the tag(s) may characterize a specific actionthat occurs in the video. For example, tags of a figure skater maycharacterize specific jumps (double Lutz, etc.) that are betteridentified in video than in a still image. Various embodiments include aspecific limit on the length of the image sequence, e.g., the video mustbe no more than 3, 5, 7 or 10 seconds.

While the embodiments illustrated by FIG. 9 include electronic glasses,these embodiments may be adapted to any device having eye trackingtechnology including cell phones, video display monitors (e.g.,computers or television screens), tablet computers, advertisingdisplays, etc. For example, the embodiments of Image Source 120Aillustrated in FIG. 9 include a television having an eye tracking cameraconfigured to determine at which part of a television screen a user islooking.

Image Source 120A optionally further includes Image Processing Logic 960configured to perform one or more steps for the purpose of tagging animage. Image Processing Logic 960 is optionally configured to reduce theload on Image Processing System 110 by performing these one or moresteps locally to Image Source 120A. For example, Image Processing Logic960 may be configured for performing initial steps in tagging of animage and then send the results of these initial steps to ImageProcessing System 110 for generation of image tags. In some embodiments,Image Processing Logic 960 is capable of completing the tagging processfor some but not necessarily all images. Image Processing Logic 960includes hardware, firmware and/or software stored on computer readablemedia. For example, some embodiments include an instance of Processor950 specifically configured to perform the functions of Image ProcessingLogic 960 discussed herein.

In some embodiments, Image Processing System 110 is configured toprovide Image Processing Logic 960 to Image Sources 120. This isoptionally via an “app store” such as the Apple App Store. Whereapplicable, Providing Image Processing Logic 960 to a member of ImageSources 120 is an optional step in the various methods illustratedherein. Processing Logic 960 can be provided as an “app” or computerinstructions that further includes other logic discussed herein, forexample the logic discussed in relation to FIG. 9.

In some embodiments, Image Processing Logic 960 is configured toidentify specific features within an image. Feature identificationincludes determining if specific points within an image are or are notpart of a feature of a given type. Types of features include, but arenot limited to, edges, corners, blobs and ridges. Generally, a featureis an “interesting” or “useful” part of an image, for the purpose ofidentifying contents of the image. Image Processing Logic 960 may beconfigured to perform one or more of a number of different featuredetection algorithms. In some embodiments, Image Processing Logic 960 isconfigured to select from among a number of different algorithms basedon available processing power and/or contents of the image. Examples ofknown feature detection algorithms include “Canny,” “Sobel,” “Harris &Stephens/Plessy,” “SUSAN,” “Shi & Tomasi,” “Level curve curvature,”FAST,” “Laplacian of Gaussian,” “Difference of Gaussians,” “Determinantof Hessian,” “MSER,” “PCBR” and Grey-level blobs.” These types ofalgorithms are executed on a computing device and other such algorithmswill be apparent to one of ordinary skill in the art. The results offeature identification include identification of a specific feature typeat a specific location within the image. This may be encoded in a“feature descriptor” or “feature vector,” etc. The results of featuredetection may also include a value representing a confidence level atwhich the feature is identified.

In some embodiments, Image Processing Logic 960 is further configured tocalculate image descriptors based on identified image features. Imagedescriptors are visual features of the contents of an image and includecharacteristics such as shape, color, texture and motion (in video).Image descriptors may be part of a specific descriptor domain, such asdescriptors related to the domains of face recognition or currencyrecognition. The derivation of image descriptors is typically based onimage features. For example, derivation of a 3-D shape descriptor may bebased on detected edge features. Image descriptors may characterize oneor more identified objects within an image.

The particular image features and image descriptors used in a particularembodiment are dependent on the particular image recognition algorithmsused. A large number of image recognition algorithms are known in theart. In some embodiments, Image Processing Logic 960 and/or ImageProcessing System 110 are configured to first attempt identification ofimage features and derivation of image descriptors of various types andthen to select from among a plurality of alternative image processingalgorithms based on the levels of confidence at which the imagedescriptors are derived. For example, if image descriptors in a facialrecognition domain are derived with a high level of confidence, then animage processing algorithm specific to facial recognition may beselected to generate image tags from these image descriptors.

In those embodiments that include Image Processing Logic 960 the task oftagging an image can be distributed between Image Sources 120 and ImageProcessing System 110. How the task is distributed may be fixed or maybe dynamic. In embodiments were the distribution is fixed specific stepsare performed consistently on specific devices. In embodiments were thedistribution is dynamic the distribution of steps may be responsive to,for example, communication bandwidth, image type (still or video)processing power on Image Source 120A, current load on Image ProcessingSystem 110, availability of image reviewers at Destinations 125, theconfidence to which steps are accomplished on Image Source 120, and/orimage descriptor data present on Image Source 120A. Any combination ofthese factors may be used to dynamically allocate distribution ofprocessing steps. For example, if the derivation of image descriptorsoccurs with a low degree of confidence (relative to a predeterminedrequirement) on Image Source 120A, then the image features and/or imagemaybe communicated to Image Processing System 110 for derivation ofimage descriptors using more powerful or alternative image processingalgorithms. In contrast, if the derivation of image descriptors occurson Image Source 120A with an adequate degree of confidence, then thisstep need not typically be performed on Image Processing System 110.

If image processing steps are successfully performed on Image Source120A by Image Processing Logic 960, the results of these steps and/orthe image may be communicated to Image Processing System 110 using I/O945. For example, in some embodiments both an image and imagedescriptors are communicated to Image Processing System 110. The imagedescriptors may be used in an attempt to automatically tag the image ormay be provided to a human image reviewer at one or more of Destinations125. The image descriptors may be used to identify a descriptor domainand this domain then used to select a member of Destinations 125 towhich the image is sent. For example, a descriptor domain of “vehicles”may be used to select an image review having expertise in vehicles. Theclassification of an image into a domain based on image descriptors mayoccur on either Image Processing System 110 or Image Processing Logic960.

In some embodiments automatic tagging of an image is attempted based onderived image descriptors. In various embodiments, this may occur usingImage Processing Logic 960 and/or Automatic Identification System 152.Classification optionally occurs by comparing the image descriptorsderived from the image with a library of image descriptors associatedwith different classes. For example, an image descriptor identifying avehicle shape may match with a previously stored image descriptorassociated with a “vehicle” class. If the class is suitable An type,scope, etc.) the identification of a class may be sufficient toautomatically select a tag for the image. For example, image descriptorsmatching those of a class “child face” may be sufficient to generate thetags “child's face.”

Typically, Image Processing System 110 includes a larger library ofimage descriptors associated with different classes relative to ImageSource 120A. These libraries are optionally stored in Memory 135 ofImage Processing System 110 or Image Source 120A, or AutomaticIdentification System 152. Libraries of image descriptors stored inImage Source 120A are optionally based on images previously processedusing Image Source 120A. For example, if several images from ImageSource 120A are identified as having descriptors and tags relating tocurrency, a library of descriptors in the currency domain/class may bestored in Memory 135 of Image Source 120A. These descriptors may beassociated with tags such as “US $5 bill.” When a new image is receivedhaving the same set of descriptors, Image Processing Logic 960 isoptionally configured to automatically tag the image using theassociated tags. While the descriptor library may be received from ImageProcessing System 110, or may be developed using image tags receivedfrom Image Processing System 110, the tagging in the above example isnot dependent on real-time communication with Image Processing System110.

In various embodiments, data characterizing relationships between imagedescriptors and classes and/or tags may be developed on Image ProcessingSystem 110, Image Source 120A, Destination 125A and/or AutomaticIdentification System 152. Once developed the data may be transferred toimprove and/or supplement the libraries at any of the other devices.

Will the systems illustrated show a client-server architecture, inalternative embodiments Image Sources 120 and Destinations 125 areconnected in a peer-to-peer architecture. In these embodiments, anycombination of the elements illustrated in Image Processing System 110may be included in Image Sources 120 and/or Destinations 125. One ofImage Sources 120 may perform the image tagging and processing tasksdiscussed herein on an image received from another of Image Sources 120.

FIG. 10 illustrates a method of processing an image at least partiallyon Image Source 120A, according to various embodiments of the invention.The methods illustrated by FIG. 10 can include a range of differentprocessing steps performed on Image Source 120A. For example, thosesteps involving image descriptors are optionally performed on ImageProcessing System 110.

In a Receive Image Step 1010 and image is received by Image Source 120A.The image may be received from a camera included in Image Source 120A,from Image Source 120B, from Network 115, from Image Processing System110, from a wireless device, from a memory device, and/or the like. Thereceived image is optionally one of a sequence of images that form avideo.

In an Identify Features Step 1020, Image Processing Logic 960 is used toidentify image features within the received image. As discussedelsewhere herein, methods of identifying image features are known in theart. Identify Features Step 1020 may apply one, two or more of thesemethods. The identification of features optionally includes a confidencelevel reflecting an estimated accuracy and/or completeness of thefeature identification.

In an optional Send Features Step 1030 the image features identified inIdentify Features Step 1020 are sent to Image Processing System 110. Thefeatures may be sent with or without the associated image and may besent via Network 115. If Send Features Step 1030 is included in themethod, the method optionally next proceeds to a Generate/Receive TagsStep 1070 in which Tags for the image are received from Image ProcessingSystem 110. Image Processing Logic 960 is optionally configured toperform Send Features Step 1030 based on a confidence level of thefeatures calculated in Identify Features Step 1020. For example, if theconfidence is below a threshold the step may be executed and both theimage and the features sent.

In an optional Derive Descriptors Step 1040 Image processing Logic 960is used to derive image descriptors from the image features identify inIdentify Features Step 1020. As discussed herein, a wide assortment ofmethods is known in the art for deriving image descriptors. In someembodiments, Derive Descriptors Step 1040 includes using more than onemethod. The derivation may include a confidence level reflecting anestimated accuracy and/or completeness of the descriptor derivation. Thetypes and content of descriptors derived is typically dependent on theimage recognition algorithm(s) used.

In an optional Send Descriptors Step 1050 the image descriptors derivedin Derive Descriptors Step 1040 are sent to Image Processing System 110.The image descriptors may be sent with or without the associated imageand may be sent via Network 115. If Send Descriptors Step 1050 isincluded in the method, the method optionally next proceeds to aGenerate/Receive Tags Step 1070 in which Tags for the image are receivedfrom Image Processing System 110. Image Processing Logic 960 isoptionally configured to perform Send Descriptors Step 1050 based on aconfidence level of the image features derived in Derive DescriptorsStep 1040. For example, if the confidence is below a threshold the stepmay be executed and both the image and the features sent.

In an optional Compare Descriptors Step 1060, the one or more imagedescriptors derived in Derive Descriptors Step 1040 are compared withone or more image descriptors stored locally. As discussed elsewhereherein, these locally stored image descriptors are associated with imageclasses and/or image tags. The comparison may include calculation of acharacteristic reflecting the quality of the match.

In some embodiments, both Send Descriptors Step 1050 and CompareDescriptors Step 160 are performed. In this case processing of the imagedescriptors can occur both on Image Source 125A and Image ProcessingSystem 110. Likewise, in some embodiments both Send Features Step 1030and Derive Descriptors Step 1040 are performed and the image featuresare processed on both systems/devices.

In an Assign/Receive Tags Step 1070 image tags characterizing the imageare generated and/or received. For example, if the image, image featuresor image descriptors have been sent to Image Processing System 110, thencorresponding tags may be received from Image Processing system 100 inAssign/Receive Tags Step 1070. If a match is found between the deriveddescriptors and the local stored descriptors in Compare Descriptors Step1060, then tags associated to the matched locally stored imagedescriptors are retrieved from local memory and assigned to the image.Tags may be both locally assigned and received for the same image. TheImage tags are optionally generated using image features and/ordescriptors, e.g., without Image Processing System 110 receiving theactual image.

In some embodiments, Assign/Receive Tags Step 1070 includes assigning aclassification to an image, sending the image and the classification toImage Processing System 110, and receiving corresponding tags back fromImage Processing System 110. The tags may be identified using themethods illustrated in FIG. 4. The classification may be used by ImageProcessing System 100 to generate the tags using AutomaticIdentification system 152 and/or a human reviewer at Destination 125A.

The assigned and/or receive tags, and/or other results, are provided inProvide Results Step 455, as discussed elsewhere herein.

FIG. 11 illustrates a method of processing an image based on imagedescriptors, according to various embodiments of the invention. Thismethod is typically performed on one of Image Sources 120. In theillustrated embodiments, Steps 1010, 1020, 1040 and 1060 are performedas described elsewhere herein. In a Classify Image Step 1110 the imagebeing processed is classified based on a match between one or more imagedescriptors derived in Derive Descriptors Step 1040 and imagedescriptors previously stored on the one of Image Sources 120. The classor classes assigned to the image is the class or classes associated withthe matched image descriptors previously stored.

In a Send Step 1120 the image and the class or classes assigned to theimage are sent to Image Processing System 110. The image is thereprocessed as described elsewhere herein to produce image tags assignedto the image. The processing optionally includes use of the class orclasses to select a human image review or to assist in automaticallytagging the image.

In a Receive Tags Step 1130 the tags assigned to the image are receivedby the one of Image Sources 120 on which Receive Image Step 1010 wasperformed. The tags are then presented in Provide Results Step 455.

FIG. 12 illustrates a method of processing an image using feedback,according to various embodiments of the invention. This method isoptionally performed on Image Source 120A and includes severalcommunications between Image Source 120A and Image Processing System 110in order to improve tagging of an image. In a Provide Image Step 1210,an image is provided from Image Source 120A to Image Processing System110.

In a Receive 1^(st) Response Step 1220 a first response is received fromImage Processing system 110. This response may include one or more imagetags. In a Provide Feedback Step 1230 feedback regarding the receivedimage tags is provided from Image Source 120A to Image Processing System110. This feedback is optionally manually entered by a human user ofImage Source 120A and may include an upgrade request as discussedelsewhere herein. Feedback may include correction to one or more of thereceived tags. For example, the feedback may include an indication thatone of the tags is not representative of the image. The feedback mayinclude a classification of the image.

In an optional Receive 2^(nd) Response Step 1240 a second response isreceived from Image Processing System 110. The second response istypically generated using the feedback provided in Provide Feedback Step1230. In one example, considering an image of a toy car, the firstresponse includes the tag “car”, the feedback includes the term “toy”and the second response includes the tags “Fisher-Price Superwagon.” Themethods illustrated by FIG. 12 are optionally used to improve theaccuracy of image tagging.

FIGS. 13 and 14 illustrates methods of providing image tags based onimage descriptors, according to various embodiments of the invention. InFIG. 13 the image descriptors are used to generate image tags that arethen communicated over a computing network to a source of the imagedescriptors. In FIG. 14 the image descriptors are used to determine aDestination 125 for an image. The methods illustrated in FIGS. 13 and 14are optionally performed in conjunction with methods illustratedelsewhere herein. For example the steps of these methods may be combinedwith those illustrated in FIG. 4.

Specifically, referring to FIG. 13, in a Receive Descriptors Step 1310one or more image descriptors characterizing an image are received atImage Processing System 110. These image descriptors are optionallyreceived without the associated image. Receiving only the descriptorstypically requires less bandwidth than receiving the image. The imagedescriptors are optionally received from Image Source 120A via Network115 and generated using the methods illustrated in FIG. 10 or 11.

In a Compare Descriptors Step 1320 the received image descriptors arecompared to one or more image descriptors previously stored at ImageProcessing System 110, e.g., stored in Memory 135. This comparison ismade to determine if any of the received descriptors match the storeddescriptors. The stored descriptors are stored in association with oneor more image tags and/or classifications. For example, one set ofstored descriptors may be associated with the image tags “oak tree.”

In a Retrieve Tags Step 1330 one or more image tags are retrievedresponsive to a match between the received descriptors and the storeddescriptors. The retrieved image tags are those associated with matchedset.

In a Provide Tags Step 1340 the retrieved image tags are provided backto the source of the received descriptors, e.g., to Image Source 120A.They may there be presented to a user or otherwise processed asdescribed elsewhere herein.

FIG. 14 illustrates methods in which an image and data characterizingthe image are processed at Image Processing Server 110. In a ReceiveImage & Data Step 1410 the image and the data characterizing the imageare received at Image Processing Server 110. The data characterizing theimage can include, for example, a classification of the image or imagedescriptors characterizing the image. The image and characterizing dataare optionally received from Image Source 125A. Receive Image & DataStep 1410 is optionally an embodiment of Receive Image Step 410 andReceive Source Data Step 420.

In a Determine Destination Step 1420 a destination for the image isdetermined based on the data characterizing the image. The destinationmay be one of Destination 125 and/or Automatic Identification System152. For example, if the data characterizing the image includes aspecific classification and the determined destination may be one ofDestination 125 being associated with a human image review havingexpertise in that classification. Determine Destination Step 1420 isoptionally an embodiment of Determine Destination Step 465.

In a Post Image Step 1430 the image, and optionally the classification,are communicated to the determined destination. In a Receive Tags Step1440 one or more image tags are received. The image tags being based onthe image and being selected to characterize the image. In a ProvideTags Step 1340 the image tags are provided to the source of the image,e.g. Image Source 125A. Post Image Step 1430 is optionally an embodimentof Post Image Step 470.

FIG. 15 illustrates methods of prioritizing image tagging, according tovarious embodiments of the invention. In these methods Image Ranker 190is used to assign a priority to an image and the priority is used todetermine how, if at all, the image is tagged. In Receive Image Step 410an image is received at Image Processing System 110 as discussedelsewhere herein. The image may be from one of Image Sources 120, andmay be received by crawling webpages for images. In some embodiments oneor more of Image Source 120 include logic configured to crawl websitesand retrieve images from these websites. Information received along withthe image may include data regarding a webpage from which the image wasretrieved. For example, the image may be received along with text andmetadata from the webpage, data indicating how often the webpage isloaded (viewed), a URL of the webpage, and/or any other data on whichimage priority may be determined as discussed elsewhere herein.

In an Assign Priority Step 1520, Image Ranker 190 is used toautomatically assign a priority to the received image. The priority isoptionally represented by a numerical value from 1-100, by a lettergrade, or the like. Priority optionally implies an (ordered) ranking ofimages. As described elsewhere herein, the priority may be determinedbased on a wide variety of factors.

In a Determine Processing Step 1530 a method of tagging (processing) theimage is determined. The determination is based on the assigned priorityof the image. In some embodiments, images with lowest priority are notprocessed (tagged) at all. The methods of tagging include automatedtagging and/or manual tagging by a human reviewer, as describedelsewhere herein.

In an optional Automatic Tagging Step 1540 the image is tagged usingAutomatic Identification System 152. Automatic Tagging Step 1540 isoptional in embodiments where the method of tagging determined inDetermine Processing Step 1530 does not include use of AutomaticIdentification System 152. Automatic Tagging Step 1540 is optionallyperformed prior to Assign Priority Step 1520. For example, an image maybe tagged using automatic Identification System 152, and a confidencelevel for the automatically generated tags may then be used in AssignPriority Step 1520 to determine a priority for manual (human) tagging.If the confidence of the automatically generated tags is high then thepriority for manual tagging may be set low, and if the confidence isrelatively low then the priority for manual tagging may be setrelatively high.

In an optional Manual Tagging Step 1550 the image is sent to one ofDestinations 125 for tagging by a human reviewer. The image may be sentwith tags generated using Automatic Identification System 152 and/or avariety of other information as described elsewhere herein. ManualTagging Step 1550 may include any of the steps illustrated by FIGS. 6-8.

In an optional Auction Tag Step 1560 an advertisement is assigned to theimage for display on a webpage. This webpage is optionally the webpagefrom which the image was obtained in Receive Image Step 410. Auction TagStep 1560 is optionally performed in real-time as a request for thewebpage is received. At that time, the tag(s) assigned to the image canbe auctioned off to the party willing to provide the greatestconsideration for placing an advertisement over or beside the image.Auction Tag Step 1560 is optionally performed using Advertising System180 and the auction process may be managed by a third party, such asGoogle's Adsence®.

In an optional Retag Step 1570, an image is retagged. Retag Step 1570may include an analysis of how often advertisement(s) assigned to theimage are clicked as compared to an expected click rate. For example ifadvertisements assigned to an image based on a first tagging are notclicked on at an expected rate, then the tags may not be an optimalrepresentation of the image. The image may be retagged in an attempt toimprove the click rate of assigned advertisements. Retag Step 1570 mayinclude any of the tagging methods disclosed herein, e.g., those methodsdiscussed in relation to FIGS. 6-8 and 15. Retag Step 1570 may use theknowledge that the tags resulting from the first tagging were notoptimal.

The methods illustrated by FIG. 15 may also be applied to imagesequences, e.g., video. The image sequence may be presented in a browseror using a variety of alternative applications. For example, video maybe provided to a member of Image Sources 120 from a website such asyoutube.com or from streaming services such as Netflix, Comcast cabletelevision, direct TV, Ruku or Hulu. Factors used to determine if animage within a video should be manually tagged include: how often thevideo is viewed and/or the estimated value of expected tags. Expectedtags may be indicated by an automatic review of the image usingAutomated Identification System 152, dialog within the video, textaccompanying the video (e.g., a description, caption or title), and/orthe like. The advertisement may be a video appended at the beginning orend of the image sequence, or spliced within the image sequence. Thus,the advertisement and video can be presented together in association.The advertisement may include an overlay placed over part of the imagesequence, typically a part including the tagged image.

FIG. 16 illustrates a method of performing a search based on an image,according to various embodiments of the invention. In this method TokenRanker 173 and Search Engine 177 are used to identify image tokens andto generate search results based on the identified image tokens. Theimage tokens are optionally weighted and the search is optionally madewithin a specific domain.

In an optional Determine Weight Step 1610, weights are determined forspecific tokens, phrases, and/or product tags. In some embodiments theseweights are based on uniqueness of the particular token, phase or tag.Uniqueness may be domain dependent and may be found by counting thenumber of times the token, phrase or tag is generated while taggingimages using Image Processing System 110. Determine Weight Step 1610 isoptionally performed using Token Ranker 173. Determine Weight Step 1610is optional in embodiments in which weights have previously beendetermined.

Receive Image Step 1620 an image is received by Image Processing System110 from one of Image Sources 120. Receive Image Step 1620 is optionallyan embodiment of Receive Image Step 410 discussed elsewhere herein.

In an Identify Tags Step 1630 image tags are identified for the receivedimage using image tagging logic. Identify Tags Step 1630 may include,for example, the various one or more of the steps used to tag imagesillustrated in FIG. 4-8, 10, 11, 14 or 15.

In an optional Identify Domain Step 1635 a domain to be searched usingthe image tags is determined. As discussed elsewhere herein. Thisdetermination can be based on a source of the image, the identity of thetags, etc.

In a Parse Tags Step 1640, the image tags are parsed to identify imagetokens within the image tags. Parse Tags Step 1640 is optionallyperformed by Token Ranker 173 and optionally includes identifying commonphrases of tokens within the image tags.

In an optional Identify Synonyms Step 1645, synonyms of the image tokensor image tags are identified and may be added to the image tokens/tagsor used as substitutes for the tokens/tags. Identify Synonyms Step 1645may be performed prior to or concurrently with Parse Tags Step 1640.

In an optional Assign Weight Step 1650 weights are assigned to the imagetokens. These weights are optionally predetermined in Determine WeightStep 1610 and may be domain dependent. Assign Weight Step 1650 isoptionally performed using Token Ranker 173.

In a Search Step 1655 a search is performed using the image tokens. Thissearch may be within a specific domain determined in Identify DomainStep 1635 and may be performed using Search Engine 177. Search Step 1655is optionally performed by comparing the image tokens to product tagsassociated with products. Alternatively, Search Step 1655 may beperformed on product names, product descriptions, unstructured data,websites, the internet, alternative domains, and/or the like. SearchStep 1655 is optionally performed using a structured query on a databaseof product inventory. Search Step 1655 optionally includes calculationof a match quality for each result/product identified in the search. Asdiscussed elsewhere herein the match quality may be dependent on theweighting assigned to image tokens and/or weighting assigned to producttags.

In a Sort Results Step 1660, results of the search(es) performed inSearch Step 1655 are ordered based on the calculated match quality. Forexample, the results may be ordered such that the highest match qualityis first.

In a Present Results Step 1670 results of the search performed in SearchStep 1655 are presented to a user, optionally at one of Image Sources120. The results are optionally presented or ranked in the ordergenerated in Sort Results Step 1660. Present Results Step 1670 isoptionally an embodiment of Provide Results Step 455 discussed elsewhereherein.

In an optional Receive Request Step 1680, a request is receivedregarding one or more product included in the presented search results.The request may be to purchase a product, to see product reviews, forfurther information regarding the product, and/or the like. A responseto Receive Request Step 1680 may be to provide a website related to arequested product.

Several embodiments are specifically illustrated and/or describedherein. However, it will be appreciated that modifications andvariations are covered by the above teachings and within the scope ofthe appended claims without departing from the spirit and intended scopethereof. For example, the images discussed herein are optionally part ofa video sequence of a video. Human image reviews may provide image tagsat Destinations 125 using audio input. The audio input can be convertedto text in real-time using audio to text conversion logic disposed onDestinations 125 and/or Image Processing System 110. Image tags areoptionally processed by spellcheck logic. As used herein, the term“Real-time” means without unnecessary delay such that a user can easilywait for completion. The systems and methods described herein areoptionally used to tag audio content, such as music or dialog. Thisaudio content may be part of a video or otherwise associated with animage. In some embodiments, audio content is automatically converted totext and this text is used to assist in manually or automatically tag animage. Text generated from audio content may be used in manners similarto those described herein for text found on a webpage including animage, to assist in tagging the image.

The embodiments discussed herein are illustrative of the presentinvention. As these embodiments of the present invention are describedwith reference to illustrations, various modifications or adaptations ofthe methods and or specific structures described may become apparent tothose skilled in the art. All such modifications, adaptations, orvariations that rely upon the teachings of the present invention, andthrough which these teachings have advanced the art, are considered tobe within the spirit and scope of the present invention. Hence, thesedescriptions and drawings should not be considered in a limiting sense,as it is understood that the present invention is in no way limited toonly the embodiments illustrated.

Computing systems referred to herein, (e.g., Image Processing System110, Images Sources 120 and Destinations 125), can comprise anintegrated circuit, a microprocessor, a personal computer, a server, adistributed computing system, a communication device, a network device,or the like, and various combinations of the same. A computing systemmay also comprise volatile and/or non-volatile memory such as randomaccess memory (RAM), dynamic random access memory (DRAM), static randomaccess memory (SRAM), magnetic media, optical media, nano-media, a harddrive, a compact disk, a digital versatile disc (DVD), and/or otherdevices configured for storing analog or digital information, such as ina database. The various examples of logic noted above can comprisehardware, firmware, or software stored on a computer-readable medium, orcombinations thereof. A computer-readable medium, as used herein,expressly excludes paper. Computer-implemented steps of the methodsnoted herein can comprise a set of instructions stored on acomputer-readable medium that when executed cause the computing systemto perform the steps. A computing system programmed to performparticular functions pursuant to instructions from program software is aspecial purpose computing system for performing those particularfunctions. Data that is manipulated by a special purpose computingsystem while performing those particular functions is at leastelectronically saved in buffers of the computing system, physicallychanging the special purpose computing system from one state to the nextwith each change to the stored data. The logic discussed herein mayinclude hardware, firmware and/or software stored on a computer readablemedium. This logic may be implemented in an electronic device to producea special purpose computing system.

What is claimed is:
 1. A search system comprising: an I/O configured tocommunicate an image over a communication network; image tagging logicconfigured to provide image tags characterizing the image, the tagsincluding a plurality of tokens; a token ranker configured to parse theimage tags so as to identify the plurality of tokens and to rank theidentified tokens; a search engine configured to perform a search usingthe image tags; response logic configured to provide results of thesearch, the results provided in an order responsive to the rank of theidentified tokens; and an electronic processor configured to execute atleast the token ranker.
 2. The system of claim 1, wherein the imagetagging logic includes an automatic identification interface configuredto communicate the image to an automatic identification system and toreceive a computer generated review of the image from the automaticidentification system, the computer generated review including the imagetags.
 3. The system of claim 2, wherein the image tagging logic includesdestination logic configured to determine a first destination to sendthe image to, for a first manual review of the image by a first humanreviewer, and includes image posting logic configured to post the imageto the first destination.
 4. The system of claim 1, wherein the imagetagging logic is also configured to determine a domain of the image. 5.The system of claim 4, wherein the domain is determined based on thecontents of the image.
 6. The system of claim 4, wherein the domain isdetermined based on a source of the image.
 7. The system of claim 4,wherein the search engine is configured to perform the search within thedomain.
 8. The system of claim 4, wherein the domain includes a specificinventory of products.
 9. The system of claim 8, wherein the products inthe specific inventory are each characterized by product tags and thesearch engine is configured to match the plurality of tokens to theproduct tags.
 10. The system of claim 9, wherein the product tags aregenerated by tagging images of the products.
 11. The system of claim 9,wherein the product tags are generated using a product name or productdescription.
 12. The system of claim 9, wherein the product tags areweighted and the order in which the results of the search is provided isresponsive to the weighting of the product tags.
 13. The system of claim1, wherein the token ranker is configured to assign values to the tokensand configured to use the assigned values to rank the identified tokens.14. The system of claim 1, wherein the token ranker is configured toassign values to the tokens based on a uniqueness of each token.
 15. Thesystem of claim 1, wherein the token ranker is configured to identifyphrases within the plurality of tokens and treat an identified phrase asa single token by assigning a single rank to the identified phrase. 16.A method of processing an image, the method comprising: determining aweighting of image tokens; receiving an image; identifying image tagscharacterizing the image; parsing the image tags to identify imagetokens within the image tags; performing a search using the tokens;sorting results of the search based on the weighting of the imagetokens; and presenting the sorted results of the search.
 17. The methodof claim 16, wherein the weighting of age tokens is determined within aspecific domain.
 18. The method of claim 16, further comprisingdetermining a weighing of phrases comprised of image tokens.
 19. Themethod of claim 16, wherein the weighting of image tokens is based on auniqueness of each image token.
 20. The method of claim 16, wherein theweighting of image tokens is based on value of the each token within aproduct classification system
 21. The method of claim 16, furthercomprising identifying synonyms of the tokens.
 22. The method of claim16, wherein the image is received from an application executed on amobile device.
 23. The method of claim 16, wherein the image tags arecharacterized using both an automatic identification system and a humanimage reviewer.
 24. The method of claim 16, further includingidentifying a domain of the image.
 25. The method of claim 24, whereinthe domain is identified based on a source of the image.
 26. The methodof claim 24, wherein the domain is identified based on a one or more ofthe image tokens.
 27. The method of claim 24, wherein the domainincludes a specific product inventory.
 28. The method of claim 24,wherein the search is performed on a database selected in response tothe domain.
 29. The method of claim 24, wherein the search is performedusing the domain and the image tokens.
 30. The method of claim 17, wherethe sorting is further based on product tags associated with productswithin the results, or on a calculation dependent on image token weightand/or product tag weight.
 31. The method of claim 17, wherein thepresented results include opportunities to purchase product or includereviews of products.